{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>Hierarchical Clustering</center></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Welcome to Lab of Hierarchical Clustering with Python using Scipy and Scikit-learn package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#generating_data\">Generating Random Data</a></li>\n",
    "                <li><a href=\"#agglomerative_clustering\">Agglomerative Clustering</a></li>\n",
    "                <li><a href=\"#dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</a></li>\n",
    "            </ol>            \n",
    "        <li><a href=\"#clustering_vehicle_dataset\">Clustering on the Vehicle Dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#data_cleaning\">Data Cleaning</a></li>\n",
    "                <li><a href=\"#clustering_using_scipy\">Clustering Using Scipy</a></li>\n",
    "                <li><a href=\"#clustering_using_skl\">Clustering using scikit-learn</a></li>\n",
    "            </ol>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"hierarchical_agglomerative\">Hierarchical Clustering - Agglomerative</h1>\n",
    "\n",
    "We will be looking at a clustering technique, which is <b>Agglomerative Hierarchical Clustering</b>. Remember that agglomerative is the bottom up approach. <br> <br>\n",
    "In this lab, we will be looking at Agglomerative clustering, which is more popular than Divisive clustering. <br> <br>\n",
    "We will also be using Complete Linkage as the Linkage Criteria. <br>\n",
    "<b> <i> NOTE: You can also try using Average Linkage wherever Complete Linkage would be used to see the difference! </i> </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from scipy import ndimage \n",
    "from scipy.cluster import hierarchy \n",
    "from scipy.spatial import distance_matrix \n",
    "from matplotlib import pyplot as plt \n",
    "from sklearn import manifold, datasets \n",
    "from sklearn.cluster import AgglomerativeClustering \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'scipy.ndimage' from '/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/scipy/ndimage/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/conda/envs/python/lib/python3.6/site-packages/scipy/ndimage/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "=========================================================\n",
       "Multi-dimensional image processing (:mod:`scipy.ndimage`)\n",
       "=========================================================\n",
       "\n",
       ".. currentmodule:: scipy.ndimage\n",
       "\n",
       "This package contains various functions for multi-dimensional image\n",
       "processing.\n",
       "\n",
       "\n",
       "Filters\n",
       "=======\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   convolve - Multi-dimensional convolution\n",
       "   convolve1d - 1-D convolution along the given axis\n",
       "   correlate - Multi-dimensional correlation\n",
       "   correlate1d - 1-D correlation along the given axis\n",
       "   gaussian_filter\n",
       "   gaussian_filter1d\n",
       "   gaussian_gradient_magnitude\n",
       "   gaussian_laplace\n",
       "   generic_filter - Multi-dimensional filter using a given function\n",
       "   generic_filter1d - 1-D generic filter along the given axis\n",
       "   generic_gradient_magnitude\n",
       "   generic_laplace\n",
       "   laplace - n-D Laplace filter based on approximate second derivatives\n",
       "   maximum_filter\n",
       "   maximum_filter1d\n",
       "   median_filter - Calculates a multi-dimensional median filter\n",
       "   minimum_filter\n",
       "   minimum_filter1d\n",
       "   percentile_filter - Calculates a multi-dimensional percentile filter\n",
       "   prewitt\n",
       "   rank_filter - Calculates a multi-dimensional rank filter\n",
       "   sobel\n",
       "   uniform_filter - Multi-dimensional uniform filter\n",
       "   uniform_filter1d - 1-D uniform filter along the given axis\n",
       "\n",
       "Fourier filters\n",
       "===============\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   fourier_ellipsoid\n",
       "   fourier_gaussian\n",
       "   fourier_shift\n",
       "   fourier_uniform\n",
       "\n",
       "Interpolation\n",
       "=============\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   affine_transform - Apply an affine transformation\n",
       "   geometric_transform - Apply an arbritrary geometric transform\n",
       "   map_coordinates - Map input array to new coordinates by interpolation\n",
       "   rotate - Rotate an array\n",
       "   shift - Shift an array\n",
       "   spline_filter\n",
       "   spline_filter1d\n",
       "   zoom - Zoom an array\n",
       "\n",
       "Measurements\n",
       "============\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   center_of_mass - The center of mass of the values of an array at labels\n",
       "   extrema - Min's and max's of an array at labels, with their positions\n",
       "   find_objects - Find objects in a labeled array\n",
       "   histogram - Histogram of the values of an array, optionally at labels\n",
       "   label - Label features in an array\n",
       "   labeled_comprehension\n",
       "   maximum\n",
       "   maximum_position\n",
       "   mean - Mean of the values of an array at labels\n",
       "   median\n",
       "   minimum\n",
       "   minimum_position\n",
       "   standard_deviation - Standard deviation of an n-D image array\n",
       "   sum - Sum of the values of the array\n",
       "   variance - Variance of the values of an n-D image array\n",
       "   watershed_ift\n",
       "\n",
       "Morphology\n",
       "==========\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   binary_closing\n",
       "   binary_dilation\n",
       "   binary_erosion\n",
       "   binary_fill_holes\n",
       "   binary_hit_or_miss\n",
       "   binary_opening\n",
       "   binary_propagation\n",
       "   black_tophat\n",
       "   distance_transform_bf\n",
       "   distance_transform_cdt\n",
       "   distance_transform_edt\n",
       "   generate_binary_structure\n",
       "   grey_closing\n",
       "   grey_dilation\n",
       "   grey_erosion\n",
       "   grey_opening\n",
       "   iterate_structure\n",
       "   morphological_gradient\n",
       "   morphological_laplace\n",
       "   white_tophat\n",
       "\n",
       "Utility\n",
       "=======\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   imread - Load an image from a file\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ndimage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
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    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'scipy.cluster.hierarchy' from '/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/scipy/cluster/hierarchy.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/conda/envs/python/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "========================================================\n",
       "Hierarchical clustering (:mod:`scipy.cluster.hierarchy`)\n",
       "========================================================\n",
       "\n",
       ".. currentmodule:: scipy.cluster.hierarchy\n",
       "\n",
       "These functions cut hierarchical clusterings into flat clusterings\n",
       "or find the roots of the forest formed by a cut by providing the flat\n",
       "cluster ids of each observation.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   fcluster\n",
       "   fclusterdata\n",
       "   leaders\n",
       "\n",
       "These are routines for agglomerative clustering.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   linkage\n",
       "   single\n",
       "   complete\n",
       "   average\n",
       "   weighted\n",
       "   centroid\n",
       "   median\n",
       "   ward\n",
       "\n",
       "These routines compute statistics on hierarchies.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   cophenet\n",
       "   from_mlab_linkage\n",
       "   inconsistent\n",
       "   maxinconsts\n",
       "   maxdists\n",
       "   maxRstat\n",
       "   to_mlab_linkage\n",
       "\n",
       "Routines for visualizing flat clusters.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   dendrogram\n",
       "\n",
       "These are data structures and routines for representing hierarchies as\n",
       "tree objects.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   ClusterNode\n",
       "   leaves_list\n",
       "   to_tree\n",
       "   cut_tree\n",
       "   optimal_leaf_ordering\n",
       "\n",
       "These are predicates for checking the validity of linkage and\n",
       "inconsistency matrices as well as for checking isomorphism of two\n",
       "flat cluster assignments.\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   is_valid_im\n",
       "   is_valid_linkage\n",
       "   is_isomorphic\n",
       "   is_monotonic\n",
       "   correspond\n",
       "   num_obs_linkage\n",
       "\n",
       "Utility routines for plotting:\n",
       "\n",
       ".. autosummary::\n",
       "   :toctree: generated/\n",
       "\n",
       "   set_link_color_palette\n",
       "\n",
       "References\n",
       "----------\n",
       "\n",
       ".. [1] \"Statistics toolbox.\" API Reference Documentation. The MathWorks.\n",
       "   http://www.mathworks.com/access/helpdesk/help/toolbox/stats/.\n",
       "   Accessed October 1, 2007.\n",
       "\n",
       ".. [2] \"Hierarchical clustering.\" API Reference Documentation.\n",
       "   The Wolfram Research, Inc.\n",
       "   https://reference.wolfram.com/language/HierarchicalClustering/tutorial/HierarchicalClustering.html.\n",
       "   Accessed October 1, 2007.\n",
       "\n",
       ".. [3] Gower, JC and Ross, GJS. \"Minimum Spanning Trees and Single Linkage\n",
       "   Cluster Analysis.\" Applied Statistics. 18(1): pp. 54--64. 1969.\n",
       "\n",
       ".. [4] Ward Jr, JH. \"Hierarchical grouping to optimize an objective\n",
       "   function.\" Journal of the American Statistical Association. 58(301):\n",
       "   pp. 236--44. 1963.\n",
       "\n",
       ".. [5] Johnson, SC. \"Hierarchical clustering schemes.\" Psychometrika.\n",
       "   32(2): pp. 241--54. 1966.\n",
       "\n",
       ".. [6] Sneath, PH and Sokal, RR. \"Numerical taxonomy.\" Nature. 193: pp.\n",
       "   855--60. 1962.\n",
       "\n",
       ".. [7] Batagelj, V. \"Comparing resemblance measures.\" Journal of\n",
       "   Classification. 12: pp. 73--90. 1995.\n",
       "\n",
       ".. [8] Sokal, RR and Michener, CD. \"A statistical method for evaluating\n",
       "   systematic relationships.\" Scientific Bulletins. 38(22):\n",
       "   pp. 1409--38. 1958.\n",
       "\n",
       ".. [9] Edelbrock, C. \"Mixture model tests of hierarchical clustering\n",
       "   algorithms: the problem of classifying everybody.\" Multivariate\n",
       "   Behavioral Research. 14: pp. 367--84. 1979.\n",
       "\n",
       ".. [10] Jain, A., and Dubes, R., \"Algorithms for Clustering Data.\"\n",
       "   Prentice-Hall. Englewood Cliffs, NJ. 1988.\n",
       "\n",
       ".. [11] Fisher, RA \"The use of multiple measurements in taxonomic\n",
       "   problems.\" Annals of Eugenics, 7(2): 179-188. 1936\n",
       "\n",
       "\n",
       "* MATLAB and MathWorks are registered trademarks of The MathWorks, Inc.\n",
       "\n",
       "* Mathematica is a registered trademark of The Wolfram Research, Inc.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hierarchy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mdistance_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Compute the distance matrix.\n",
       "\n",
       "Returns the matrix of all pair-wise distances.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x : (M, K) array_like\n",
       "    Matrix of M vectors in K dimensions.\n",
       "y : (N, K) array_like\n",
       "    Matrix of N vectors in K dimensions.\n",
       "p : float, 1 <= p <= infinity\n",
       "    Which Minkowski p-norm to use.\n",
       "threshold : positive int\n",
       "    If ``M * N * K`` > `threshold`, algorithm uses a Python loop instead\n",
       "    of large temporary arrays.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "result : (M, N) ndarray\n",
       "    Matrix containing the distance from every vector in `x` to every vector\n",
       "    in `y`.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from scipy.spatial import distance_matrix\n",
       ">>> distance_matrix([[0,0],[0,1]], [[1,0],[1,1]])\n",
       "array([[ 1.        ,  1.41421356],\n",
       "       [ 1.41421356,  1.        ]])\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/scipy/spatial/kdtree.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance_matrix?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'sklearn.manifold' from '/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/manifold/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/conda/envs/python/lib/python3.6/site-packages/sklearn/manifold/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m   The :mod:`sklearn.manifold` module implements data embedding techniques.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "manifold?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "jupyter": {
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    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'sklearn.datasets' from '/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/sklearn/datasets/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/conda/envs/python/lib/python3.6/site-packages/sklearn/datasets/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m  \n",
       "The :mod:`sklearn.datasets` module includes utilities to load datasets,\n",
       "including methods to load and fetch popular reference datasets. It also\n",
       "features some artificial data generators.\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "jupyter": {
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    }
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m\n",
       "\u001b[0mAgglomerativeClustering\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_clusters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0maffinity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'euclidean'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmemory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconnectivity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcompute_full_tree\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'auto'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlinkage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ward'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpooling_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'deprecated'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Agglomerative Clustering\n",
       "\n",
       "Recursively merges the pair of clusters that minimally increases\n",
       "a given linkage distance.\n",
       "\n",
       "Read more in the :ref:`User Guide <hierarchical_clustering>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "n_clusters : int, default=2\n",
       "    The number of clusters to find.\n",
       "\n",
       "affinity : string or callable, default: \"euclidean\"\n",
       "    Metric used to compute the linkage. Can be \"euclidean\", \"l1\", \"l2\",\n",
       "    \"manhattan\", \"cosine\", or 'precomputed'.\n",
       "    If linkage is \"ward\", only \"euclidean\" is accepted.\n",
       "\n",
       "memory : None, str or object with the joblib.Memory interface, optional\n",
       "    Used to cache the output of the computation of the tree.\n",
       "    By default, no caching is done. If a string is given, it is the\n",
       "    path to the caching directory.\n",
       "\n",
       "connectivity : array-like or callable, optional\n",
       "    Connectivity matrix. Defines for each sample the neighboring\n",
       "    samples following a given structure of the data.\n",
       "    This can be a connectivity matrix itself or a callable that transforms\n",
       "    the data into a connectivity matrix, such as derived from\n",
       "    kneighbors_graph. Default is None, i.e, the\n",
       "    hierarchical clustering algorithm is unstructured.\n",
       "\n",
       "compute_full_tree : bool or 'auto' (optional)\n",
       "    Stop early the construction of the tree at n_clusters. This is\n",
       "    useful to decrease computation time if the number of clusters is\n",
       "    not small compared to the number of samples. This option is\n",
       "    useful only when specifying a connectivity matrix. Note also that\n",
       "    when varying the number of clusters and using caching, it may\n",
       "    be advantageous to compute the full tree.\n",
       "\n",
       "linkage : {\"ward\", \"complete\", \"average\", \"single\"}, optional             (default=\"ward\")\n",
       "    Which linkage criterion to use. The linkage criterion determines which\n",
       "    distance to use between sets of observation. The algorithm will merge\n",
       "    the pairs of cluster that minimize this criterion.\n",
       "\n",
       "    - ward minimizes the variance of the clusters being merged.\n",
       "    - average uses the average of the distances of each observation of\n",
       "      the two sets.\n",
       "    - complete or maximum linkage uses the maximum distances between\n",
       "      all observations of the two sets.\n",
       "    - single uses the minimum of the distances between all observations\n",
       "      of the two sets.\n",
       "\n",
       "pooling_func : callable, default='deprecated'\n",
       "    Ignored.\n",
       "\n",
       "    .. deprecated:: 0.20\n",
       "        ``pooling_func`` has been deprecated in 0.20 and will be removed\n",
       "        in 0.22.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "labels_ : array [n_samples]\n",
       "    cluster labels for each point\n",
       "\n",
       "n_leaves_ : int\n",
       "    Number of leaves in the hierarchical tree.\n",
       "\n",
       "n_components_ : int\n",
       "    The estimated number of connected components in the graph.\n",
       "\n",
       "children_ : array-like, shape (n_samples-1, 2)\n",
       "    The children of each non-leaf node. Values less than `n_samples`\n",
       "    correspond to leaves of the tree which are the original samples.\n",
       "    A node `i` greater than or equal to `n_samples` is a non-leaf\n",
       "    node and has children `children_[i - n_samples]`. Alternatively\n",
       "    at the i-th iteration, children[i][0] and children[i][1]\n",
       "    are merged to form node `n_samples + i`\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.cluster import AgglomerativeClustering\n",
       ">>> import numpy as np\n",
       ">>> X = np.array([[1, 2], [1, 4], [1, 0],\n",
       "...               [4, 2], [4, 4], [4, 0]])\n",
       ">>> clustering = AgglomerativeClustering().fit(X)\n",
       ">>> clustering # doctest: +NORMALIZE_WHITESPACE\n",
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='ward', memory=None, n_clusters=2,\n",
       "            pooling_func='deprecated')\n",
       ">>> clustering.labels_\n",
       "array([1, 1, 1, 0, 0, 0])\n",
       "\u001b[0;31mFile:\u001b[0m           ~/conda/envs/python/lib/python3.6/site-packages/sklearn/cluster/hierarchical.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     FeatureAgglomeration\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "AgglomerativeClustering?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mmake_blobs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mn_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcenters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcluster_std\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcenter_box\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m10.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Generate isotropic Gaussian blobs for clustering.\n",
       "\n",
       "Read more in the :ref:`User Guide <sample_generators>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "n_samples : int or array-like, optional (default=100)\n",
       "    If int, it is the total number of points equally divided among\n",
       "    clusters.\n",
       "    If array-like, each element of the sequence indicates\n",
       "    the number of samples per cluster.\n",
       "\n",
       "n_features : int, optional (default=2)\n",
       "    The number of features for each sample.\n",
       "\n",
       "centers : int or array of shape [n_centers, n_features], optional\n",
       "    (default=None)\n",
       "    The number of centers to generate, or the fixed center locations.\n",
       "    If n_samples is an int and centers is None, 3 centers are generated.\n",
       "    If n_samples is array-like, centers must be\n",
       "    either None or an array of length equal to the length of n_samples.\n",
       "\n",
       "cluster_std : float or sequence of floats, optional (default=1.0)\n",
       "    The standard deviation of the clusters.\n",
       "\n",
       "center_box : pair of floats (min, max), optional (default=(-10.0, 10.0))\n",
       "    The bounding box for each cluster center when centers are\n",
       "    generated at random.\n",
       "\n",
       "shuffle : boolean, optional (default=True)\n",
       "    Shuffle the samples.\n",
       "\n",
       "random_state : int, RandomState instance or None (default)\n",
       "    Determines random number generation for dataset creation. Pass an int\n",
       "    for reproducible output across multiple function calls.\n",
       "    See :term:`Glossary <random_state>`.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "X : array of shape [n_samples, n_features]\n",
       "    The generated samples.\n",
       "\n",
       "y : array of shape [n_samples]\n",
       "    The integer labels for cluster membership of each sample.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.datasets.samples_generator import make_blobs\n",
       ">>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,\n",
       "...                   random_state=0)\n",
       ">>> print(X.shape)\n",
       "(10, 2)\n",
       ">>> y\n",
       "array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])\n",
       ">>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,\n",
       "...                   random_state=0)\n",
       ">>> print(X.shape)\n",
       "(10, 2)\n",
       ">>> y\n",
       "array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])\n",
       "\n",
       "See also\n",
       "--------\n",
       "make_classification: a more intricate variant\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/sklearn/datasets/samples_generator.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_blobs?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"generating_data\">Generating Random Data</h3>\n",
    "We will be generating a set of data using the <b>make_blobs</b> class. <br> <br>\n",
    "Input these parameters into make_blobs:\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Choose a number from 10-1500 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Choose arrays of x,y coordinates for generating the centers. Have 1-10 centers (ex. centers=[[1,1], [2,5]]) </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. The larger the number, the further apart the clusters</li>\n",
    "    <ul> <li> Choose a number between 0.5-1.5 </li> </ul>\n",
    "</ul> <br>\n",
    "Save the result to <b>X1</b> and <b>y1</b>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1, y1 = make_blobs(n_samples=50, centers=[[4,4], [-2, -1], [1, 1], [10,4]], cluster_std=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the scatter plot of the randomly generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f843f99b5c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X1[:, 0], X1[:, 1], marker='o') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h3 id=\"agglomerative_clustering\">Agglomerative Clustering</h3>\n",
    "We will start by clustering the random data points we just created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <b> Agglomerative Clustering </b> class will require two inputs:\n",
    "<ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 </li> </ul>\n",
    "    <li> <b>linkage</b>: Which linkage criterion to use. The linkage criterion determines which distance to use between sets of observation. The algorithm will merge the pairs of cluster that minimize this criterion. </li>\n",
    "    <ul> \n",
    "        <li> Value will be: 'complete' </li> \n",
    "        <li> <b>Note</b>: It is recommended you try everything with 'average' as well </li>\n",
    "    </ul>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> agglom </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 4, linkage = 'average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='average', memory=None,\n",
       "            n_clusters=4, pooling_func='deprecated')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgglomerativeClustering(n_clusters = 4, linkage = 'average')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='complete', memory=None,\n",
       "            n_clusters=4, pooling_func='deprecated')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AgglomerativeClustering(n_clusters = 4, linkage = 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit the model with <b> X2 </b> and <b> y2 </b> from the generated data above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(affinity='euclidean', compute_full_tree='auto',\n",
       "            connectivity=None, linkage='average', memory=None,\n",
       "            n_clusters=4, pooling_func='deprecated')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom.fit(X1,y1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to show the clustering! <br>\n",
    "Remember to read the code and comments to gain more understanding on how the plotting works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(6,4))\n",
    "\n",
    "# These two lines of code are used to scale the data points down,\n",
    "# Or else the data points will be scattered very far apart.\n",
    "\n",
    "# Create a minimum and maximum range of X1.\n",
    "x_min, x_max = np.min(X1, axis=0), np.max(X1, axis=0)\n",
    "\n",
    "# Get the average distance for X1.\n",
    "X1 = (X1 - x_min) / (x_max - x_min)\n",
    "\n",
    "# This loop displays all of the datapoints.\n",
    "for i in range(X1.shape[0]):\n",
    "    # Replace the data points with their respective cluster value \n",
    "    # (ex. 0) and is color coded with a colormap (plt.cm.spectral)\n",
    "    plt.text(X1[i, 0], X1[i, 1], str(y1[i]),\n",
    "             color=plt.cm.nipy_spectral(agglom.labels_[i] / 10.),\n",
    "             fontdict={'weight': 'bold', 'size': 9})\n",
    "    \n",
    "# Remove the x ticks, y ticks, x and y axis\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "#plt.axis('off')\n",
    "\n",
    "\n",
    "\n",
    "# Display the plot of the original data before clustering\n",
    "plt.scatter(X1[:, 0], X1[:, 1], marker='.')\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<h3 id=\"dendrogram\">Dendrogram Associated for the Agglomerative Hierarchical Clustering</h3>\n",
    "Remember that a <b>distance matrix</b> contains the <b> distance from each point to every other point of a dataset </b>. <br>\n",
    "Use the function <b> distance_matrix, </b> which requires <b>two inputs</b>. Use the Feature Matrix, <b> X2 </b> as both inputs and save the distance matrix to a variable called <b> dist_matrix </b> <br> <br>\n",
    "Remember that the distance values are symmetric, with a diagonal of 0's. This is one way of making sure your matrix is correct. <br> (print out dist_matrix to make sure it's correct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.3044463  0.16877882 ... 0.41285412 0.60937267 0.73815899]\n",
      " [0.3044463  0.         0.22050932 ... 0.11808032 0.31021745 0.43603749]\n",
      " [0.16877882 0.22050932 0.         ... 0.29733255 0.48644353 0.617683  ]\n",
      " ...\n",
      " [0.41285412 0.11808032 0.29733255 ... 0.         0.19651867 0.32595324]\n",
      " [0.60937267 0.31021745 0.48644353 ... 0.19651867 0.         0.13123949]\n",
      " [0.73815899 0.43603749 0.617683   ... 0.32595324 0.13123949 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(X1,X1) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the <b> linkage </b> class from hierarchy, pass in the parameters:\n",
    "<ul>\n",
    "    <li> The distance matrix </li>\n",
    "    <li> 'complete' for complete linkage </li>\n",
    "</ul> <br>\n",
    "Save the result to a variable called <b> Z </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:1: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "Z = hierarchy.linkage(dist_matrix, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Hierarchical clustering is typically visualized as a dendrogram as shown in the following cell. Each merge is represented by a horizontal line. The y-coordinate of the horizontal line is the similarity of the two clusters that were merged, where cities are viewed as singleton clusters. \n",
    "By moving up from the bottom layer to the top node, a dendrogram allows us to reconstruct the history of merges that resulted in the depicted clustering. \n",
    "\n",
    "Next, we will save the dendrogram to a variable called <b>dendro</b>. In doing this, the dendrogram will also be displayed.\n",
    "Using the <b> dendrogram </b> class from hierarchy, pass in the parameter:\n",
    "<ul> <li> Z </li> </ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dendro = hierarchy.dendrogram(Z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "We used __complete__ linkage for our case, change it to __average__ linkage to see how the dendogram changes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:2: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# write your code here\n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "Double-click __here__ for the solution.\n",
    "\n",
    "<!-- Your answer is below:\n",
    "    \n",
    "Z = hierarchy.linkage(dist_matrix, 'average')\n",
    "dendro = hierarchy.dendrogram(Z)\n",
    "\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h1 id=\"clustering_vehicle_dataset\">Clustering on Vehicle dataset</h1>\n",
    "\n",
    "Imagine that an automobile manufacturer has developed prototypes for a new vehicle. Before introducing the new model into its range, the manufacturer wants to determine which existing vehicles on the market are most like the prototypes--that is, how vehicles can be grouped, which group is the most similar with the model, and therefore which models they will be competing against.\n",
    "\n",
    "Our objective here, is to use clustering methods, to find the most distinctive clusters of vehicles. It will summarize the existing vehicles and help manufacturers to make decision about the supply of new models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data\n",
    "To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
    "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-10-05 01:50:19--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 17774 (17K) [text/csv]\n",
      "Saving to: ‘cars_clus.csv’\n",
      "\n",
      "cars_clus.csv       100%[===================>]  17.36K  --.-KB/s    in 0.02s   \n",
      "\n",
      "2019-10-05 01:50:20 (843 KB/s) - ‘cars_clus.csv’ saved [17774/17774]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O cars_clus.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "lets read dataset to see what features the manufacturer has collected about the existing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset:  (159, 16)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.000</td>\n",
       "      <td>21.500</td>\n",
       "      <td>1.800</td>\n",
       "      <td>140.000</td>\n",
       "      <td>101.200</td>\n",
       "      <td>67.300</td>\n",
       "      <td>172.400</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.200</td>\n",
       "      <td>28.000</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.000</td>\n",
       "      <td>28.400</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>108.100</td>\n",
       "      <td>70.300</td>\n",
       "      <td>192.900</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.200</td>\n",
       "      <td>25.000</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Acura</td>\n",
       "      <td>CL</td>\n",
       "      <td>14.114</td>\n",
       "      <td>18.225</td>\n",
       "      <td>0.000</td>\n",
       "      <td>$null$</td>\n",
       "      <td>3.200</td>\n",
       "      <td>225.000</td>\n",
       "      <td>106.900</td>\n",
       "      <td>70.600</td>\n",
       "      <td>192.000</td>\n",
       "      <td>3.470</td>\n",
       "      <td>17.200</td>\n",
       "      <td>26.000</td>\n",
       "      <td>2.647</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.000</td>\n",
       "      <td>42.000</td>\n",
       "      <td>3.500</td>\n",
       "      <td>210.000</td>\n",
       "      <td>114.600</td>\n",
       "      <td>71.400</td>\n",
       "      <td>196.600</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.000</td>\n",
       "      <td>22.000</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.000</td>\n",
       "      <td>23.990</td>\n",
       "      <td>1.800</td>\n",
       "      <td>150.000</td>\n",
       "      <td>102.600</td>\n",
       "      <td>68.200</td>\n",
       "      <td>178.000</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.400</td>\n",
       "      <td>27.000</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale   type   price engine_s horsepow wheelbas  \\\n",
       "0    Acura  Integra  16.919  16.360  0.000  21.500    1.800  140.000  101.200   \n",
       "1    Acura       TL  39.384  19.875  0.000  28.400    3.200  225.000  108.100   \n",
       "2    Acura       CL  14.114  18.225  0.000  $null$    3.200  225.000  106.900   \n",
       "3    Acura       RL   8.588  29.725  0.000  42.000    3.500  210.000  114.600   \n",
       "4     Audi       A4  20.397  22.255  0.000  23.990    1.800  150.000  102.600   \n",
       "\n",
       "    width   length curb_wgt fuel_cap     mpg lnsales  partition  \n",
       "0  67.300  172.400    2.639   13.200  28.000   2.828        0.0  \n",
       "1  70.300  192.900    3.517   17.200  25.000   3.673        0.0  \n",
       "2  70.600  192.000    3.470   17.200  26.000   2.647        0.0  \n",
       "3  71.400  196.600    3.850   18.000  22.000   2.150        0.0  \n",
       "4  68.200  178.000    2.998   16.400  27.000   3.015        0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = 'cars_clus.csv'\n",
    "\n",
    "#Read csv\n",
    "pdf = pd.read_csv(filename)\n",
    "print (\"Shape of dataset: \", pdf.shape)\n",
    "\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature sets include  price in thousands (price), engine size (engine_s), horsepower (horsepow), wheelbase (wheelbas), width (width), length (length), curb weight (curb_wgt), fuel capacity (fuel_cap) and fuel efficiency (mpg)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"data_cleaning\">Data Cleaning</h2>\n",
    "lets simply clear the dataset by dropping the rows that have null value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataset before cleaning:  2544\n",
      "Shape of dataset after cleaning:  1872\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0  \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0  \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0  \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0  \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Shape of dataset before cleaning: \", pdf.size)\n",
    "pdf[[ 'sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']] = pdf[['sales', 'resale', 'type', 'price', 'engine_s',\n",
    "       'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap',\n",
    "       'mpg', 'lnsales']].apply(pd.to_numeric, errors='coerce')\n",
    "pdf = pdf.dropna()\n",
    "pdf = pdf.reset_index(drop=True)\n",
    "print (\"Shape of dataset after cleaning: \", pdf.size)\n",
    "pdf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection\n",
    "Lets select our feature set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "featureset = pdf[['engine_s',  'horsepow', 'wheelbas', 'width', 'length', 'curb_wgt', 'fuel_cap', 'mpg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "Now we can normalize the feature set. __MinMaxScaler__ transforms features by scaling each feature to a given range. It is by default (0, 1). That is, this estimator scales and translates each feature individually such that it is between zero and one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11428571, 0.21518987, 0.18655098, 0.28143713, 0.30625832,\n",
       "        0.2310559 , 0.13364055, 0.43333333],\n",
       "       [0.31428571, 0.43037975, 0.3362256 , 0.46107784, 0.5792277 ,\n",
       "        0.50372671, 0.31797235, 0.33333333],\n",
       "       [0.35714286, 0.39240506, 0.47722343, 0.52694611, 0.62849534,\n",
       "        0.60714286, 0.35483871, 0.23333333],\n",
       "       [0.11428571, 0.24050633, 0.21691974, 0.33532934, 0.38082557,\n",
       "        0.34254658, 0.28110599, 0.4       ],\n",
       "       [0.25714286, 0.36708861, 0.34924078, 0.80838323, 0.56724368,\n",
       "        0.5173913 , 0.37788018, 0.23333333]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "x = featureset.values #returns a numpy array\n",
    "min_max_scaler = MinMaxScaler()\n",
    "feature_mtx = min_max_scaler.fit_transform(x)\n",
    "feature_mtx [0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m     \n",
       "Transforms features by scaling each feature to a given range.\n",
       "\n",
       "This estimator scales and translates each feature individually such\n",
       "that it is in the given range on the training set, e.g. between\n",
       "zero and one.\n",
       "\n",
       "The transformation is given by::\n",
       "\n",
       "    X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))\n",
       "    X_scaled = X_std * (max - min) + min\n",
       "\n",
       "where min, max = feature_range.\n",
       "\n",
       "This transformation is often used as an alternative to zero mean,\n",
       "unit variance scaling.\n",
       "\n",
       "Read more in the :ref:`User Guide <preprocessing_scaler>`.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "feature_range : tuple (min, max), default=(0, 1)\n",
       "    Desired range of transformed data.\n",
       "\n",
       "copy : boolean, optional, default True\n",
       "    Set to False to perform inplace row normalization and avoid a\n",
       "    copy (if the input is already a numpy array).\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "min_ : ndarray, shape (n_features,)\n",
       "    Per feature adjustment for minimum.\n",
       "\n",
       "scale_ : ndarray, shape (n_features,)\n",
       "    Per feature relative scaling of the data.\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *scale_* attribute.\n",
       "\n",
       "data_min_ : ndarray, shape (n_features,)\n",
       "    Per feature minimum seen in the data\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *data_min_*\n",
       "\n",
       "data_max_ : ndarray, shape (n_features,)\n",
       "    Per feature maximum seen in the data\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *data_max_*\n",
       "\n",
       "data_range_ : ndarray, shape (n_features,)\n",
       "    Per feature range ``(data_max_ - data_min_)`` seen in the data\n",
       "\n",
       "    .. versionadded:: 0.17\n",
       "       *data_range_*\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from sklearn.preprocessing import MinMaxScaler\n",
       "\n",
       ">>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]\n",
       ">>> scaler = MinMaxScaler()\n",
       ">>> print(scaler.fit(data))\n",
       "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
       ">>> print(scaler.data_max_)\n",
       "[ 1. 18.]\n",
       ">>> print(scaler.transform(data))\n",
       "[[0.   0.  ]\n",
       " [0.25 0.25]\n",
       " [0.5  0.5 ]\n",
       " [1.   1.  ]]\n",
       ">>> print(scaler.transform([[2, 2]]))\n",
       "[[1.5 0. ]]\n",
       "\n",
       "See also\n",
       "--------\n",
       "minmax_scale: Equivalent function without the estimator API.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "NaNs are treated as missing values: disregarded in fit, and maintained in\n",
       "transform.\n",
       "\n",
       "For a comparison of the different scalers, transformers, and normalizers,\n",
       "see :ref:`examples/preprocessing/plot_all_scaling.py\n",
       "<sphx_glr_auto_examples_preprocessing_plot_all_scaling.py>`.\n",
       "\u001b[0;31mFile:\u001b[0m           ~/conda/envs/python/lib/python3.6/site-packages/sklearn/preprocessing/data.py\n",
       "\u001b[0;31mType:\u001b[0m           type\n",
       "\u001b[0;31mSubclasses:\u001b[0m     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "MinMaxScaler?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_scipy\">Clustering using Scipy</h2>\n",
    "In this part we use Scipy package to cluster the dataset:  \n",
    "First, we calculate the distance matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "leng = feature_mtx.shape[0]\n",
    "D = scipy.zeros([leng,leng])\n",
    "for i in range(leng):\n",
    "    for j in range(leng):\n",
    "        D[i,j] = scipy.spatial.distance.euclidean(feature_mtx[i], feature_mtx[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "zeros(shape, dtype=float, order='C')\n",
       "\n",
       "Return a new array of given shape and type, filled with zeros.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "shape : int or tuple of ints\n",
       "    Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n",
       "dtype : data-type, optional\n",
       "    The desired data-type for the array, e.g., `numpy.int8`.  Default is\n",
       "    `numpy.float64`.\n",
       "order : {'C', 'F'}, optional, default: 'C'\n",
       "    Whether to store multi-dimensional data in row-major\n",
       "    (C-style) or column-major (Fortran-style) order in\n",
       "    memory.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "out : ndarray\n",
       "    Array of zeros with the given shape, dtype, and order.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "zeros_like : Return an array of zeros with shape and type of input.\n",
       "empty : Return a new uninitialized array.\n",
       "ones : Return a new array setting values to one.\n",
       "full : Return a new array of given shape filled with value.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> np.zeros(5)\n",
       "array([ 0.,  0.,  0.,  0.,  0.])\n",
       "\n",
       ">>> np.zeros((5,), dtype=int)\n",
       "array([0, 0, 0, 0, 0])\n",
       "\n",
       ">>> np.zeros((2, 1))\n",
       "array([[ 0.],\n",
       "       [ 0.]])\n",
       "\n",
       ">>> s = (2,2)\n",
       ">>> np.zeros(s)\n",
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.]])\n",
       "\n",
       ">>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype\n",
       "array([(0, 0), (0, 0)],\n",
       "      dtype=[('x', '<i4'), ('y', '<i4')])\n",
       "\u001b[0;31mType:\u001b[0m      builtin_function_or_method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scipy.zeros?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspatial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meuclidean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Computes the Euclidean distance between two 1-D arrays.\n",
       "\n",
       "The Euclidean distance between 1-D arrays `u` and `v`, is defined as\n",
       "\n",
       ".. math::\n",
       "\n",
       "   {||u-v||}_2\n",
       "\n",
       "   \\left(\\sum{(w_i |(u_i - v_i)|^2)}\\right)^{1/2}\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "u : (N,) array_like\n",
       "    Input array.\n",
       "v : (N,) array_like\n",
       "    Input array.\n",
       "w : (N,) array_like, optional\n",
       "    The weights for each value in `u` and `v`. Default is None,\n",
       "    which gives each value a weight of 1.0\n",
       "\n",
       "Returns\n",
       "-------\n",
       "euclidean : double\n",
       "    The Euclidean distance between vectors `u` and `v`.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from scipy.spatial import distance\n",
       ">>> distance.euclidean([1, 0, 0], [0, 1, 0])\n",
       "1.4142135623730951\n",
       ">>> distance.euclidean([1, 1, 0], [0, 1, 0])\n",
       "1.0\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/scipy/spatial/distance.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scipy.spatial.distance.euclidean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In agglomerative clustering, at each iteration, the algorithm must update the distance matrix to reflect the distance of the newly formed cluster with the remaining clusters in the forest. \n",
    "The following methods are supported in Scipy for calculating the distance between the newly formed cluster and each:\n",
    "    - single\n",
    "    - complete\n",
    "    - average\n",
    "    - weighted\n",
    "    - centroid\n",
    "    \n",
    "    \n",
    "We use __complete__ for our case, but feel free to change it to see how the results change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.6/site-packages/ipykernel_launcher.py:3: ClusterWarning: scipy.cluster: The symmetric non-negative hollow observation matrix looks suspiciously like an uncondensed distance matrix\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "import pylab\n",
    "import scipy.cluster.hierarchy\n",
    "Z = hierarchy.linkage(D, 'complete')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essentially, Hierarchical clustering does not require a pre-specified number of clusters. However, in some applications we want a partition of disjoint clusters just as in flat clustering.\n",
    "So you can use a cutting line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5,  5,  6,  5,  4,  6,  5,  5,  5,  5,  5,  4,  4,  5,  1,  6,\n",
       "        5,  5,  5,  4,  2, 11,  6,  6,  5,  6,  5,  1,  6,  6, 10,  9,  8,\n",
       "        9,  3,  5,  1,  7,  6,  5,  3,  5,  3,  8,  7,  9,  2,  6,  6,  5,\n",
       "        4,  2,  1,  6,  5,  2,  7,  5,  5,  5,  4,  4,  3,  2,  6,  6,  5,\n",
       "        7,  4,  7,  6,  6,  5,  3,  5,  5,  6,  5,  4,  4,  1,  6,  5,  5,\n",
       "        5,  6,  4,  5,  4,  1,  6,  5,  6,  6,  5,  5,  5,  7,  7,  7,  2,\n",
       "        2,  1,  2,  6,  5,  1,  1,  1,  7,  8,  1,  1,  6,  1,  1],\n",
       "      dtype=int32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "max_d = 3\n",
    "clusters = fcluster(Z, max_d, criterion='distance')\n",
    "clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mfcluster\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inconsistent'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mR\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmonocrit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Form flat clusters from the hierarchical clustering defined by\n",
       "the given linkage matrix.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "Z : ndarray\n",
       "    The hierarchical clustering encoded with the matrix returned\n",
       "    by the `linkage` function.\n",
       "t : float\n",
       "    The threshold to apply when forming flat clusters.\n",
       "criterion : str, optional\n",
       "    The criterion to use in forming flat clusters. This can\n",
       "    be any of the following values:\n",
       "\n",
       "      ``inconsistent`` : \n",
       "          If a cluster node and all its\n",
       "          descendants have an inconsistent value less than or equal\n",
       "          to `t` then all its leaf descendants belong to the\n",
       "          same flat cluster. When no non-singleton cluster meets\n",
       "          this criterion, every node is assigned to its own\n",
       "          cluster. (Default)\n",
       "\n",
       "      ``distance`` : \n",
       "          Forms flat clusters so that the original\n",
       "          observations in each flat cluster have no greater a\n",
       "          cophenetic distance than `t`.\n",
       "\n",
       "      ``maxclust`` : \n",
       "          Finds a minimum threshold ``r`` so that\n",
       "          the cophenetic distance between any two original\n",
       "          observations in the same flat cluster is no more than\n",
       "          ``r`` and no more than `t` flat clusters are formed.\n",
       "\n",
       "      ``monocrit`` : \n",
       "          Forms a flat cluster from a cluster node c\n",
       "          with index i when ``monocrit[j] <= t``.\n",
       "\n",
       "          For example, to threshold on the maximum mean distance\n",
       "          as computed in the inconsistency matrix R with a\n",
       "          threshold of 0.8 do::\n",
       "\n",
       "              MR = maxRstat(Z, R, 3)\n",
       "              cluster(Z, t=0.8, criterion='monocrit', monocrit=MR)\n",
       "\n",
       "      ``maxclust_monocrit`` : \n",
       "          Forms a flat cluster from a\n",
       "          non-singleton cluster node ``c`` when ``monocrit[i] <=\n",
       "          r`` for all cluster indices ``i`` below and including\n",
       "          ``c``. ``r`` is minimized such that no more than ``t``\n",
       "          flat clusters are formed. monocrit must be\n",
       "          monotonic. For example, to minimize the threshold t on\n",
       "          maximum inconsistency values so that no more than 3 flat\n",
       "          clusters are formed, do::\n",
       "\n",
       "              MI = maxinconsts(Z, R)\n",
       "              cluster(Z, t=3, criterion='maxclust_monocrit', monocrit=MI)\n",
       "\n",
       "depth : int, optional\n",
       "    The maximum depth to perform the inconsistency calculation.\n",
       "    It has no meaning for the other criteria. Default is 2.\n",
       "R : ndarray, optional\n",
       "    The inconsistency matrix to use for the 'inconsistent'\n",
       "    criterion. This matrix is computed if not provided.\n",
       "monocrit : ndarray, optional\n",
       "    An array of length n-1. `monocrit[i]` is the\n",
       "    statistics upon which non-singleton i is thresholded. The\n",
       "    monocrit vector must be monotonic, i.e. given a node c with\n",
       "    index i, for all node indices j corresponding to nodes\n",
       "    below c, ``monocrit[i] >= monocrit[j]``.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "fcluster : ndarray\n",
       "    An array of length ``n``. ``T[i]`` is the flat cluster number to\n",
       "    which original observation ``i`` belongs.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fcluster?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, you can determine the number of clusters directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 2, 2, 3, 1, 3, 3, 3, 3, 2, 1,\n",
       "       5, 3, 3, 3, 3, 3, 1, 3, 3, 4, 4, 4, 4, 2, 3, 1, 3, 3, 3, 2, 3, 2,\n",
       "       4, 3, 4, 1, 3, 3, 3, 2, 1, 1, 3, 3, 1, 3, 3, 3, 3, 2, 2, 2, 1, 3,\n",
       "       3, 3, 3, 2, 3, 3, 3, 3, 2, 3, 3, 3, 3, 2, 2, 1, 3, 3, 3, 3, 3, 2,\n",
       "       3, 2, 1, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1, 3, 3, 1, 1, 1,\n",
       "       3, 4, 1, 1, 3, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.cluster.hierarchy import fcluster\n",
    "k = 5\n",
    "clusters = fcluster(Z, k, criterion='maxclust')\n",
    "clusters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plot the dendrogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pylab.figure(figsize=(18,50))\n",
    "def llf(id):\n",
    "    return '[%s %s %s]' % (pdf['manufact'][id], pdf['model'][id], int(float(pdf['type'][id])) )\n",
    "    \n",
    "dendro = hierarchy.dendrogram(Z,  leaf_label_func=llf, leaf_rotation=0, leaf_font_size =12, orientation = 'right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mhierarchy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdendrogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtruncate_mode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcolor_threshold\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mget_leaves\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcount_sort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdistance_sort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshow_leaf_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mno_plot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mno_labels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleaf_font_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleaf_rotation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mleaf_label_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshow_contracted\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlink_color_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mabove_threshold_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Plot the hierarchical clustering as a dendrogram.\n",
       "\n",
       "The dendrogram illustrates how each cluster is\n",
       "composed by drawing a U-shaped link between a non-singleton\n",
       "cluster and its children.  The top of the U-link indicates a\n",
       "cluster merge.  The two legs of the U-link indicate which clusters\n",
       "were merged.  The length of the two legs of the U-link represents\n",
       "the distance between the child clusters.  It is also the\n",
       "cophenetic distance between original observations in the two\n",
       "children clusters.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "Z : ndarray\n",
       "    The linkage matrix encoding the hierarchical clustering to\n",
       "    render as a dendrogram. See the ``linkage`` function for more\n",
       "    information on the format of ``Z``.\n",
       "p : int, optional\n",
       "    The ``p`` parameter for ``truncate_mode``.\n",
       "truncate_mode : str, optional\n",
       "    The dendrogram can be hard to read when the original\n",
       "    observation matrix from which the linkage is derived is\n",
       "    large. Truncation is used to condense the dendrogram. There\n",
       "    are several modes:\n",
       "\n",
       "    ``None``\n",
       "      No truncation is performed (default).\n",
       "      Note: ``'none'`` is an alias for ``None`` that's kept for\n",
       "      backward compatibility.\n",
       "\n",
       "    ``'lastp'``\n",
       "      The last ``p`` non-singleton clusters formed in the linkage are the\n",
       "      only non-leaf nodes in the linkage; they correspond to rows\n",
       "      ``Z[n-p-2:end]`` in ``Z``. All other non-singleton clusters are\n",
       "      contracted into leaf nodes.\n",
       "\n",
       "    ``'level'``\n",
       "      No more than ``p`` levels of the dendrogram tree are displayed.\n",
       "      A \"level\" includes all nodes with ``p`` merges from the last merge.\n",
       "\n",
       "      Note: ``'mtica'`` is an alias for ``'level'`` that's kept for\n",
       "      backward compatibility.\n",
       "\n",
       "color_threshold : double, optional\n",
       "    For brevity, let :math:`t` be the ``color_threshold``.\n",
       "    Colors all the descendent links below a cluster node\n",
       "    :math:`k` the same color if :math:`k` is the first node below\n",
       "    the cut threshold :math:`t`. All links connecting nodes with\n",
       "    distances greater than or equal to the threshold are colored\n",
       "    blue. If :math:`t` is less than or equal to zero, all nodes\n",
       "    are colored blue. If ``color_threshold`` is None or\n",
       "    'default', corresponding with MATLAB(TM) behavior, the\n",
       "    threshold is set to ``0.7*max(Z[:,2])``.\n",
       "get_leaves : bool, optional\n",
       "    Includes a list ``R['leaves']=H`` in the result\n",
       "    dictionary. For each :math:`i`, ``H[i] == j``, cluster node\n",
       "    ``j`` appears in position ``i`` in the left-to-right traversal\n",
       "    of the leaves, where :math:`j < 2n-1` and :math:`i < n`.\n",
       "orientation : str, optional\n",
       "    The direction to plot the dendrogram, which can be any\n",
       "    of the following strings:\n",
       "\n",
       "    ``'top'``\n",
       "      Plots the root at the top, and plot descendent links going downwards.\n",
       "      (default).\n",
       "\n",
       "    ``'bottom'``\n",
       "      Plots the root at the bottom, and plot descendent links going\n",
       "      upwards.\n",
       "\n",
       "    ``'left'``\n",
       "      Plots the root at the left, and plot descendent links going right.\n",
       "\n",
       "    ``'right'``\n",
       "      Plots the root at the right, and plot descendent links going left.\n",
       "\n",
       "labels : ndarray, optional\n",
       "    By default ``labels`` is None so the index of the original observation\n",
       "    is used to label the leaf nodes.  Otherwise, this is an :math:`n`\n",
       "    -sized list (or tuple). The ``labels[i]`` value is the text to put\n",
       "    under the :math:`i` th leaf node only if it corresponds to an original\n",
       "    observation and not a non-singleton cluster.\n",
       "count_sort : str or bool, optional\n",
       "    For each node n, the order (visually, from left-to-right) n's\n",
       "    two descendent links are plotted is determined by this\n",
       "    parameter, which can be any of the following values:\n",
       "\n",
       "    ``False``\n",
       "      Nothing is done.\n",
       "\n",
       "    ``'ascending'`` or ``True``\n",
       "      The child with the minimum number of original objects in its cluster\n",
       "      is plotted first.\n",
       "\n",
       "    ``'descendent'``\n",
       "      The child with the maximum number of original objects in its cluster\n",
       "      is plotted first.\n",
       "\n",
       "    Note ``distance_sort`` and ``count_sort`` cannot both be True.\n",
       "distance_sort : str or bool, optional\n",
       "    For each node n, the order (visually, from left-to-right) n's\n",
       "    two descendent links are plotted is determined by this\n",
       "    parameter, which can be any of the following values:\n",
       "\n",
       "    ``False``\n",
       "      Nothing is done.\n",
       "\n",
       "    ``'ascending'`` or ``True``\n",
       "      The child with the minimum distance between its direct descendents is\n",
       "      plotted first.\n",
       "\n",
       "    ``'descending'``\n",
       "      The child with the maximum distance between its direct descendents is\n",
       "      plotted first.\n",
       "\n",
       "    Note ``distance_sort`` and ``count_sort`` cannot both be True.\n",
       "show_leaf_counts : bool, optional\n",
       "     When True, leaf nodes representing :math:`k>1` original\n",
       "     observation are labeled with the number of observations they\n",
       "     contain in parentheses.\n",
       "no_plot : bool, optional\n",
       "    When True, the final rendering is not performed. This is\n",
       "    useful if only the data structures computed for the rendering\n",
       "    are needed or if matplotlib is not available.\n",
       "no_labels : bool, optional\n",
       "    When True, no labels appear next to the leaf nodes in the\n",
       "    rendering of the dendrogram.\n",
       "leaf_rotation : double, optional\n",
       "    Specifies the angle (in degrees) to rotate the leaf\n",
       "    labels. When unspecified, the rotation is based on the number of\n",
       "    nodes in the dendrogram (default is 0).\n",
       "leaf_font_size : int, optional\n",
       "    Specifies the font size (in points) of the leaf labels. When\n",
       "    unspecified, the size based on the number of nodes in the\n",
       "    dendrogram.\n",
       "leaf_label_func : lambda or function, optional\n",
       "    When leaf_label_func is a callable function, for each\n",
       "    leaf with cluster index :math:`k < 2n-1`. The function\n",
       "    is expected to return a string with the label for the\n",
       "    leaf.\n",
       "\n",
       "    Indices :math:`k < n` correspond to original observations\n",
       "    while indices :math:`k \\geq n` correspond to non-singleton\n",
       "    clusters.\n",
       "\n",
       "    For example, to label singletons with their node id and\n",
       "    non-singletons with their id, count, and inconsistency\n",
       "    coefficient, simply do::\n",
       "\n",
       "        # First define the leaf label function.\n",
       "        def llf(id):\n",
       "            if id < n:\n",
       "                return str(id)\n",
       "            else:\n",
       "                return '[%d %d %1.2f]' % (id, count, R[n-id,3])\n",
       "        # The text for the leaf nodes is going to be big so force\n",
       "        # a rotation of 90 degrees.\n",
       "        dendrogram(Z, leaf_label_func=llf, leaf_rotation=90)\n",
       "\n",
       "show_contracted : bool, optional\n",
       "    When True the heights of non-singleton nodes contracted\n",
       "    into a leaf node are plotted as crosses along the link\n",
       "    connecting that leaf node.  This really is only useful when\n",
       "    truncation is used (see ``truncate_mode`` parameter).\n",
       "link_color_func : callable, optional\n",
       "    If given, `link_color_function` is called with each non-singleton id\n",
       "    corresponding to each U-shaped link it will paint. The function is\n",
       "    expected to return the color to paint the link, encoded as a matplotlib\n",
       "    color string code. For example::\n",
       "\n",
       "        dendrogram(Z, link_color_func=lambda k: colors[k])\n",
       "\n",
       "    colors the direct links below each untruncated non-singleton node\n",
       "    ``k`` using ``colors[k]``.\n",
       "ax : matplotlib Axes instance, optional\n",
       "    If None and `no_plot` is not True, the dendrogram will be plotted\n",
       "    on the current axes.  Otherwise if `no_plot` is not True the\n",
       "    dendrogram will be plotted on the given ``Axes`` instance. This can be\n",
       "    useful if the dendrogram is part of a more complex figure.\n",
       "above_threshold_color : str, optional\n",
       "    This matplotlib color string sets the color of the links above the\n",
       "    color_threshold. The default is 'b'.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "R : dict\n",
       "    A dictionary of data structures computed to render the\n",
       "    dendrogram. Its has the following keys:\n",
       "\n",
       "    ``'color_list'``\n",
       "      A list of color names. The k'th element represents the color of the\n",
       "      k'th link.\n",
       "\n",
       "    ``'icoord'`` and ``'dcoord'``\n",
       "      Each of them is a list of lists. Let ``icoord = [I1, I2, ..., Ip]``\n",
       "      where ``Ik = [xk1, xk2, xk3, xk4]`` and ``dcoord = [D1, D2, ..., Dp]``\n",
       "      where ``Dk = [yk1, yk2, yk3, yk4]``, then the k'th link painted is\n",
       "      ``(xk1, yk1)`` - ``(xk2, yk2)`` - ``(xk3, yk3)`` - ``(xk4, yk4)``.\n",
       "\n",
       "    ``'ivl'``\n",
       "      A list of labels corresponding to the leaf nodes.\n",
       "\n",
       "    ``'leaves'``\n",
       "      For each i, ``H[i] == j``, cluster node ``j`` appears in position\n",
       "      ``i`` in the left-to-right traversal of the leaves, where\n",
       "      :math:`j < 2n-1` and :math:`i < n`. If ``j`` is less than ``n``, the\n",
       "      ``i``-th leaf node corresponds to an original observation.\n",
       "      Otherwise, it corresponds to a non-singleton cluster.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "linkage, set_link_color_palette\n",
       "\n",
       "Notes\n",
       "-----\n",
       "It is expected that the distances in ``Z[:,2]`` be monotonic, otherwise\n",
       "crossings appear in the dendrogram.\n",
       "\n",
       "Examples\n",
       "--------\n",
       ">>> from scipy.cluster import hierarchy\n",
       ">>> import matplotlib.pyplot as plt\n",
       "\n",
       "A very basic example:\n",
       "\n",
       ">>> ytdist = np.array([662., 877., 255., 412., 996., 295., 468., 268.,\n",
       "...                    400., 754., 564., 138., 219., 869., 669.])\n",
       ">>> Z = hierarchy.linkage(ytdist, 'single')\n",
       ">>> plt.figure()\n",
       ">>> dn = hierarchy.dendrogram(Z)\n",
       "\n",
       "Now plot in given axes, improve the color scheme and use both vertical and\n",
       "horizontal orientations:\n",
       "\n",
       ">>> hierarchy.set_link_color_palette(['m', 'c', 'y', 'k'])\n",
       ">>> fig, axes = plt.subplots(1, 2, figsize=(8, 3))\n",
       ">>> dn1 = hierarchy.dendrogram(Z, ax=axes[0], above_threshold_color='y',\n",
       "...                            orientation='top')\n",
       ">>> dn2 = hierarchy.dendrogram(Z, ax=axes[1],\n",
       "...                            above_threshold_color='#bcbddc',\n",
       "...                            orientation='right')\n",
       ">>> hierarchy.set_link_color_palette(None)  # reset to default after use\n",
       ">>> plt.show()\n",
       "\u001b[0;31mFile:\u001b[0m      ~/conda/envs/python/lib/python3.6/site-packages/scipy/cluster/hierarchy.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hierarchy.dendrogram?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"clustering_using_skl\">Clustering using scikit-learn</h2>\n",
    "Lets redo it again, but this time using scikit-learn package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         0.57777143 0.75455727 ... 0.28530295 0.24917241 0.18879995]\n",
      " [0.57777143 0.         0.22798938 ... 0.36087756 0.66346677 0.62201282]\n",
      " [0.75455727 0.22798938 0.         ... 0.51727787 0.81786095 0.77930119]\n",
      " ...\n",
      " [0.28530295 0.36087756 0.51727787 ... 0.         0.41797928 0.35720492]\n",
      " [0.24917241 0.66346677 0.81786095 ... 0.41797928 0.         0.15212198]\n",
      " [0.18879995 0.62201282 0.77930119 ... 0.35720492 0.15212198 0.        ]]\n"
     ]
    }
   ],
   "source": [
    "dist_matrix = distance_matrix(feature_mtx,feature_mtx) \n",
    "print(dist_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can use the 'AgglomerativeClustering' function from scikit-learn library to cluster the dataset. The AgglomerativeClustering performs a hierarchical clustering using a bottom up approach. The linkage criteria determines the metric used for the merge strategy:\n",
    "\n",
    "- Ward minimizes the sum of squared differences within all clusters. It is a variance-minimizing approach and in this sense is similar to the k-means objective function but tackled with an agglomerative hierarchical approach.\n",
    "- Maximum or complete linkage minimizes the maximum distance between observations of pairs of clusters.\n",
    "- Average linkage minimizes the average of the distances between all observations of pairs of clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 1, 2, 3, 1, 2, 2, 2, 2, 2, 3, 3, 2, 1, 1, 2, 2, 2, 5, 1,\n",
       "       4, 1, 1, 2, 1, 2, 1, 1, 1, 5, 0, 0, 0, 3, 2, 1, 2, 1, 2, 3, 2, 3,\n",
       "       0, 3, 0, 1, 1, 1, 2, 3, 1, 1, 1, 2, 1, 1, 2, 2, 2, 3, 3, 3, 1, 1,\n",
       "       1, 2, 1, 2, 2, 1, 1, 2, 3, 2, 3, 1, 2, 3, 5, 1, 1, 2, 3, 2, 1, 3,\n",
       "       2, 3, 1, 1, 2, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       2, 0, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agglom = AgglomerativeClustering(n_clusters = 6, linkage = 'complete')\n",
    "agglom.fit(feature_mtx)\n",
    "agglom.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, we can add a new field to our dataframe to show the cluster of each row:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufact</th>\n",
       "      <th>model</th>\n",
       "      <th>sales</th>\n",
       "      <th>resale</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>horsepow</th>\n",
       "      <th>wheelbas</th>\n",
       "      <th>width</th>\n",
       "      <th>length</th>\n",
       "      <th>curb_wgt</th>\n",
       "      <th>fuel_cap</th>\n",
       "      <th>mpg</th>\n",
       "      <th>lnsales</th>\n",
       "      <th>partition</th>\n",
       "      <th>cluster_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Acura</td>\n",
       "      <td>Integra</td>\n",
       "      <td>16.919</td>\n",
       "      <td>16.360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.50</td>\n",
       "      <td>1.8</td>\n",
       "      <td>140.0</td>\n",
       "      <td>101.2</td>\n",
       "      <td>67.3</td>\n",
       "      <td>172.4</td>\n",
       "      <td>2.639</td>\n",
       "      <td>13.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.828</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Acura</td>\n",
       "      <td>TL</td>\n",
       "      <td>39.384</td>\n",
       "      <td>19.875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.40</td>\n",
       "      <td>3.2</td>\n",
       "      <td>225.0</td>\n",
       "      <td>108.1</td>\n",
       "      <td>70.3</td>\n",
       "      <td>192.9</td>\n",
       "      <td>3.517</td>\n",
       "      <td>17.2</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Acura</td>\n",
       "      <td>RL</td>\n",
       "      <td>8.588</td>\n",
       "      <td>29.725</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>3.5</td>\n",
       "      <td>210.0</td>\n",
       "      <td>114.6</td>\n",
       "      <td>71.4</td>\n",
       "      <td>196.6</td>\n",
       "      <td>3.850</td>\n",
       "      <td>18.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A4</td>\n",
       "      <td>20.397</td>\n",
       "      <td>22.255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.99</td>\n",
       "      <td>1.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>68.2</td>\n",
       "      <td>178.0</td>\n",
       "      <td>2.998</td>\n",
       "      <td>16.4</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Audi</td>\n",
       "      <td>A6</td>\n",
       "      <td>18.780</td>\n",
       "      <td>23.555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.95</td>\n",
       "      <td>2.8</td>\n",
       "      <td>200.0</td>\n",
       "      <td>108.7</td>\n",
       "      <td>76.1</td>\n",
       "      <td>192.0</td>\n",
       "      <td>3.561</td>\n",
       "      <td>18.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.933</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufact    model   sales  resale  type  price  engine_s  horsepow  \\\n",
       "0    Acura  Integra  16.919  16.360   0.0  21.50       1.8     140.0   \n",
       "1    Acura       TL  39.384  19.875   0.0  28.40       3.2     225.0   \n",
       "2    Acura       RL   8.588  29.725   0.0  42.00       3.5     210.0   \n",
       "3     Audi       A4  20.397  22.255   0.0  23.99       1.8     150.0   \n",
       "4     Audi       A6  18.780  23.555   0.0  33.95       2.8     200.0   \n",
       "\n",
       "   wheelbas  width  length  curb_wgt  fuel_cap   mpg  lnsales  partition  \\\n",
       "0     101.2   67.3   172.4     2.639      13.2  28.0    2.828        0.0   \n",
       "1     108.1   70.3   192.9     3.517      17.2  25.0    3.673        0.0   \n",
       "2     114.6   71.4   196.6     3.850      18.0  22.0    2.150        0.0   \n",
       "3     102.6   68.2   178.0     2.998      16.4  27.0    3.015        0.0   \n",
       "4     108.7   76.1   192.0     3.561      18.5  22.0    2.933        0.0   \n",
       "\n",
       "   cluster_  \n",
       "0         1  \n",
       "1         2  \n",
       "2         2  \n",
       "3         1  \n",
       "4         2  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf['cluster_'] = agglom.labels_\n",
    "pdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mpg')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y8/MpKyvjtdde46WXXjpa07AqSZIkJU9L9sRtOqJujPG4IfnILE+AxsY/7a0zbdo0li5dSmFhIZMnT2bZsmVt1p49ezY1NTXU1NSwceNGbr/9dgC6du16Gk9zagysHWDZsmXMmDGDzZs3A/Dqq69y8803M2bMGL73ve8B8Nvf/paf/OQnvP/976ehoYH/+3//LwMGDGD06NH07NmTq666ihtvvJGBAwdy7733MmrUqHP3QJIkSZJOKL+D0tWJ6l555ZUsXryY3bt3A7kdSN6ovLyc1atXA7ktM4948cUXGTRoEDNmzKCqqoo1a9bQrVs3Dhw4cLTN5MmTWbhwIQ0NDQBs3bqVHTt2tMdjnRQDawdoaWnhW9/6Fg8//DD79++nZ8+erF27lo997GNs2bKFrVu3UlZWxvbt26murmbhwoVccsklPPXUU1x88cXMmjWLqVOn8vWvf50PfehD9OvX71w/kiRJkqQTKO4cyMbcqGR7yK2+C70Kjz/FeOjQodxzzz1MmDCB4cOHc+eddx5z/q677uLb3/42H/zgB9m1a9fR40888QTDhg2jsrKSF154gVtvvZVevXoxfvx4hg0bxt13382f//mfM23aNMaNG8fll1/O1KlTjwm0b/R3f/d3lJaWcvDgQUpLS5kzZ84Zfwehvb7MjjRq1Kh4ZFWrRMpmYM8W2PY8NDYQO3Wl7Oq/4iNV1zOgrIxbb72VRx55hD59+rB//35SqRR/8Rd/wa233sqNN97I2LFjWbBgAevXr+eHP/whAwcOPNdPJEmSJOkkrF+/niFDhhz9+yvPNtGciRQVnPl7rA3NkU7pwOyx7bTscEK8+TsDCCGsjjG+ZVqpI6xnqqkBVv8Ifr8U6l+GpgOEPa8w/7Y/Y++Laxh5+RBuu+02evTowZAhQ7jiiitYsWIFRUVFPPjgg7z66qt88Ytf5JJLLuHpp582rEqSJEnnsQmlafY1tU+tvU3wodJ0+xQ7T3V4YA0hpEMIz4UQfnb47zkhhK0hhJrDP9d0dB86TDYDa34GDbugW28o7AEFXaCwB1Ov/TOqn/09k/s28oExo/nyl79MU1MTH/jAB+jXrx/z589n5MiRfPnLX6a6uppZs2ZRUFBwrp9IkiRJ0hmo7JumUxoOtbY9kzVLpCWVoSmvhZZUhixttzvUEumcztV7Nzsb29rMBNYD3d9w7BsxxgfOwr071p4tsH87dO/zllN5eWnunv4R/teCx/hfD3ybcR8cT3l5OZ07dz66qha035LXkiRJks69rvmBqZfm8f21rQzoHkmFQCTSlNfKvi6vs6/LQWKIEAOESIiBCw524YKDXenUmkcgkI2RHQfhvw/Lo2v+uzsvdGhgDSGUAtcCc4E7T9D8/LPtech/+1HRmZ+4mouuuYP/52/Xcc01fxpIHjdu3NHfDaySJEnSO8v7+6bZUB9ZuS1D7x4t7Oi5l4MFzaRiIK81TYo/ZYAskX1dDrKn6+t0aSqgz94e7Nibx9iL0rz/XT66Ch0/JfibwN8Bb9456PMhhDUhhIUhhJ5tXRhC+OsQwqoQwqqdO3d2cDdPU2MDpN8+sHbuVMD0yeP4afXTZ7FTkiRJks6lEAI3XZrH0NIMz1+wk9fTLXRqyaOgNe+YsAqQIlDQmkenljxez2vh+Qt2Mqw0w02X5jm4RQcG1hDCdcCOGOPqN536NvBeoBLYBjzY1vUxxu/EGEfFGEf17t27o7p5ZjoXQab5uE2+8T8+xkf+fMJZ6pAkSZKkJGgIrWQG1jOkOE1syuNAc6A503bb5gwcaA7EpjyGFKfJDKynIbSe3Q4nVEeOsI4HqkIIm4EfApNCCItijNtjjJkYYxb4Z2BMB/ahY134Pmg5fmBNZVrhwqFnqUOSJEmSzrUYI79s2U0k8t6u+UwYkGZorxR5KTjQ/NafvBQM7ZViwoA07+2aTyTyny27z2g/1zlz5vDAA6e+bNDevXtZsGDBKV938OBBrr32Wi677DKGDh3KrFmzTrlGWzossMYYZ8cYS2OM5cAngGUxxr8IIVz4hmYfA9Z2VB86XM8B0L0vNOxu+3zD7tz5nqVnt1+SJEmSzpldsYVXs01ccHjJoPxUYEC3FFf0z+PKsjTjLkrzgQtzn1eWpbmifx4DuqXIT+WmAF9AHluzTeyKLWe976cTWI8E67vuuosXXniB5557jhUrVvDv//7vZ9yfc7EP69dDCH8IIawBPgx84Rz0oX2k0lBxHRSVwP4dcGgvNB/Mfe7fkTtecV2unSRJkqR3hXWZBtKh7QVW81KB7gWBHp1yn3mpt7YJIZAOuTon67HHHqOiooLhw4czffr0Y85NnDiRVatWAbBr1y7Ky8tz/Vy3jjFjxlBZWUlFRQW1tbXMmjWLTZs2UVlZyd133w3A/PnzGT16NBUVFdx///0AbN68mSFDhnDHHXcwcuRIdu7cyYc//GEACgoKGDlyJHV1dSfd/7dzNra1Icb4FPDU4d+nH7fx+aZTEbz/47CnDratyy3E1LkoNw24Z6lhVZIkSXoXaYlZ1mYOcAH5Z1TnAvJZmznA+Lwe5IfjjzOuW7eOuXPnsmLFCkpKSqivr+fhhx8+4T0eeeQRZs6cyS233EJzczOZTIZ58+axdu1aampqAKiurqa2tpaVK1cSY6Sqqorly5dTVlbGhg0bePTRR98yIrt3715++tOfMnPmzNP/Ag47K4H1HS+Vhl4Dcz+SJEmS3rUayZIB8s5whd+8EMjGXL38E0yMXbZsGVOnTqWkpASA4uLik7rHuHHjmDt3LnV1ddxwww0MHjz4LW2qq6uprq5mxIgRADQ0NFBbW0tZWRkDBw5k7Nixx7RvbW3lk5/8JDNmzGDQoEEn1Y/jORdTgiVJkiTpHaklRtpzM5qWk1h4KcZ43C1w8vLyyGZzO402NjYePT5t2jSWLl1KYWEhkydPZtmyZW3Wnj17NjU1NdTU1LBx40Zuv/12ALp27fqW9n/913/N4MGD+Zu/+ZsT9vtkGFglSZIkqZ3kh8Dpr+3bdr0TufLKK1m8eDG7d+cWg62vrz/mfHl5OatX53YbXbJkydHjL774IoMGDWLGjBlUVVWxZs0aunXrxoEDB462mTx5MgsXLqShIfc+7datW9mxY0eb/bj33nvZt28f3/zmN0/tIY/DKcGSJEmS1E46kyINtMZ4RtOCW2MkdbjeiQwdOpR77rmHCRMmkE6nGTFixNGFlSC3eu9NN93E448/zqRJk44ef+KJJ1i0aBH5+fn069eP++67j+LiYsaPH8+wYcOYMmUK8+fPZ/369YwbNw6AoqIiFi1aRDp97Fo9dXV1zJ07l8suu4yRI0cC8PnPf55Pf/rTp/0dAIQz2dvnbBk1alQ8sqqVJEmSJCXF+vXrGTJkyDHHnmqp5w+ZA/QKBaddd3ds5vJ0Nybmn9z7qOeTtr6zEMLqGOOoN7d1SrAkSZIktaOh6SIy8U/7k75FzEBsyX22dTpGMjFX593OKcGSJEmS1I5KQj79U53YGZvpQT7ESIrXSWd3k2I/ITYDAYjEUECW7mRSvcjSFUJgH630T3WiJJzZ1jjvBI6wSpIkSVI7CiFwVX4vUgQOZg9QkH2BgswG0rEeYiBSSAyFRAohBtKxnoLMBgqyL3Awe4AUueuPt/Lvu4WBVZIkSZLaWc9UPlemMnTLbqAle4gsXYihM4Q0HAmiIUBIE0NnsnShJXuIbtkNXJnK0DPl6CoYWCVJkiSp3e1q3UpDdiOXpIrpnOrCQTIcjBmyb3qtNRvhYMxwkAydU124JFVMQ3Yju1q3npuOJ4zvsEqSJElSOzqUbaCudQOdKSKVSvM+8jkYs+yIzezMNvHGtZgCgT6pTvQJBXQJKSCQjWnqWjfQNXUBhal398JLjrBKkiRJUjuJMfJKy/OkyCMVjuxVGugS0pSnCnl/3gVU5nXn8nQ3KvO68/687pSnCukS0uQWYoJUSJMij1dann/7lYZPYM6cOTzwwAOnfN3evXtZsGDBad3z6quvZvjw4QwdOpTPfvazZDJtr4J8KgyskiRJktROXo/7OBQb6BQK2zyfIlBAisKQpoAUKdpeWKlTKORQbOD1uK8ju/sWpxNYY4xks1kWL17M73//e9auXcvOnTv50Y9+dMb9MbBKkiRJUjupz2wjtFPMCqSoz2w7qbaPPfYYFRUVDB8+nOnTpx9zbuLEiaxatQqAXbt2UV5eDsC6desYM2YMlZWVVFRUUFtby6xZs9i0aROVlZXcfffdAMyfP5/Ro0dTUVHB/fffD8DmzZsZMmQId9xxByNHjmTLli10794dgNbWVpqbm9tllWPfYZUkSZKkdnIgs4d8OrVLrXw60ZDdc8J269atY+7cuaxYsYKSkhLq6+t5+OGHT3jdI488wsyZM7nllltobm4mk8kwb9481q5dS01NDQDV1dXU1taycuVKYoxUVVWxfPlyysrK2LBhA48++ugxI7KTJ09m5cqVTJkyhalTp57+wx/mCKskSZIktYNMbKWFRlKkT9z4JKRI0xwbycTW47ZbtmwZU6dOpaSkBIDi4uKTqj9u3Di+8pWv8LWvfY2XX36ZwsK3TmOurq6murqaESNGMHLkSF544QVqa2sBGDhwIGPHjj2m/X/8x3+wbds2mpqaWLZs2Un143gMrJIkSZLUDiJZAqFdpsICR+tEsse/b4zHvWdeXh7ZbK5GY2Pj0ePTpk1j6dKlFBYWMnny5DYDZoyR2bNnU1NTQ01NDRs3buT2228HoGvXrm3er3PnzlRVVfGTn/zk+A94EgyskiRJktQOAiki8bRX9n2zI3VO9E7slVdeyeLFi9m9ezcA9fX1x5wvLy9n9erVACxZsuTo8RdffJFBgwYxY8YMqqqqWLNmDd26dePAgQNH20yePJmFCxfS0NAAwNatW9mxY8db+tDQ0MC2bbn3bVtbW/n5z3/OZZdddqqP/Ba+wypJkiRJ7SAd8sinM1kypNshamXJUBA6kw7HrzV06FDuueceJkyYQDqdZsSIEUcXVgK46667uOmmm3j88ceZNGnS0eNPPPEEixYtIj8/n379+nHfffdRXFzM+PHjGTZsGFOmTGH+/PmsX7+ecePGAVBUVMSiRYtIp4+d9vz6669TVVVFU1MTmUyGSZMm8dnPfvaMv4PQXum/I40aNSoeWdVKkiRJkpJi/fr1DBky5Ojfr7SsZ09mO51D29NlT0VjfJ2e6b6U5Q85cePzyJu/M4AQwuoY46g3t3VKsCRJkiS1k+L0hSd85/RkRbIUpy9sl1rnKwOrJEmSJLWTruECCkMRTfHQGdVpiocoDN3oGi5op56dnwyskiRJktROQgiU5b+PLK1kY+a0amRjhiytlOUPabcVh89XBlZJkiRJakeFqSJK8y6lkYZTDq3ZmKGRBkrzLqUwVdRBPTx/uEqwJEmSJLWzkrz+ANS1biAV8+gUCk94TVM8RJZWBuQNOXr9u52BVZIkSZI6QElef7qmLuCVluc5FA8QSJFPJ1KkCSEQYyRLhhaaiGQpDEWU5b/PkdU3cEqwJEmSJHWQwlQRlxSM5uKCkfQMJRTU76Dr8yvo9tyTdH1+BZ327KBn6M3FBSO5pGB0u4XVOXPm8MADD5zydXv37mXBggVndO+qqiqGDRt2RjWOMLBKkiRJUgcKIVDUkkfZmrVc/MLL9D/QiX6tveh/oBPvfX4zZWv+QFFLXiIWWDqdwBpjJJvNbeXzL//yLxQVtd8IsYFVkiRJkjpSNgNrfgYNu6Bbb1JdepLqVESqS0/o3id3fM3Pcu1O02OPPUZFRQXDhw9n+vTpx5ybOHEiq1atAmDXrl2Ul5cDsG7dOsaMGUNlZSUVFRXU1tYya9YsNm3aRGVlJXfffTcA8+fPZ/To0VRUVHD//fcDsHnzZoYMGcIdd9zByJEj2bJlCw0NDTz00EPce++9p/0cb+Y7rJIkSZLUkfZsgf3bc+G0LUW9cuf31EGvgadcft26dcydO5cVK1ZQUlJCfX09Dz/88Amve+SRR5g5cya33HILzc3NZDIZ5s2bx9q1a6mpqQGgurqa2tpaVq5cSYyRqqoqli9fTllZGRs2bODRRx89OiL7hS98gb/927+lS5cup/wMbxvYnncAACAASURBVMcRVkmSJEnqSNueh/yC47fJL4Bt606r/LJly5g6dSolJSUAFBcXn9R148aN4ytf+Qpf+9rXePnllyksfOtKxtXV1VRXVzNixAhGjhzJCy+8QG1tLQADBw5k7NixANTU1LBx40Y+9rGPndYzvB0DqyRJkiR1pMYGSJ8gsKYLcu1OQ4zxuO+/5uXlHX3HtLGx8ejxadOmsXTpUgoLC5k8eTLLli1rs/bs2bOpqak5Gkpvv/12ALp27Xq03W9/+1tWr15NeXk5V1xxBX/84x+ZOHHiaT3PGxlYJUmSJKkjdS6CTPPx22Sac+1Ow5VXXsnixYvZvXs3APX19cecLy8vZ/Xq1QAsWbLk6PEXX3yRQYMGMWPGDKqqqlizZg3dunXjwIEDR9tMnjyZhQsX0tCQC9Nbt25lx44db+nD5z73OV599VU2b97Mb37zGy655BKeeuqp03qeN/IdVkmSJEnqSBe+D15ZBzsPwY562FkPLa2Qnwe9i6FPMRRmYOjQ0yo/dOhQ7rnnHiZMmEA6nWbEiBFHF1YCuOuuu7jpppt4/PHHmTRp0tHjTzzxBIsWLSI/P59+/fpx3333UVxczPjx4xk2bBhTpkxh/vz5rF+/nnHjxgFQVFTEokWLSKfTZ/SVnKwQYzwrNzoTo0aNikdWtZIkSZKkpFi/fj1Dhgx5+wZ1dfCrZfDj70HzIUgV5IJqKkA25oJrthkKCuHGv4QPT4LS0rPV/XOire8shLA6xjjqzW0dYZUkSZKk9tbYCEuXws9/Dnl5cOn7of4laGqAdBpCGmIGMhno1BuK3wNPL4cnl8E110BVFXTufK6f4pwzsEqSJElSe9qxAx56CF57LTdamnc4dl30Pji0Hxp2Qmsz5BVAUW8o7A4hBaVdobU1F3J/9zu4807o8zZb4bxLuOiSJEmSJLWXHTvgK1+B/fuhvPxPYRVyobRLD+gzGC4amvvs0iN3/Ii8vNx1+/fn6rSxwNG7iYFVkiRJktpDY2NuZLW5+cxHRvv0ydV56KFc3XcpA6skSZIktYelS3PTgNtrGm+fPrl6S5e2T73zkIFVkiRJks5UXV3u3dP2XuG3tDRXt66ufeueJwyskiRJknSmnnoq9/5pXjuva3uk5tNPn9Jlc+bM4YEHHjjl2+3du5cFCxac8nUAEydO5NJLL6WyspLKykp2tMP7twZWSZIkSToThw7lAmXfvh1Tv2/fXP1Dhzqm/hucTmCNMZLNZgH4wQ9+QE1NDTU1NfRph6nRBlZJkiRJOhN1dZDNQn5+x9TPz89td7N169s2eeyxx6ioqGD48OFMnz79mHMTJ05k1apVAOzatYvy8nIA1q1bx5gxY6isrKSiooLa2lpmzZrFpk2bqKys5O677wZg/vz5jB49moqKCu6//34ANm/ezJAhQ7jjjjsYOXIkW7Zs6YAHdx9WSZIkSTozRwJrR4oxd5+LL37LqXXr1jF37lxWrFhBSUkJ9fX1PPzwwycs+cgjjzBz5kxuueUWmpubyWQyzJs3j7Vr11JTUwNAdXU1tbW1rFy5khgjVVVVLF++nLKyMjZs2MCjjz56zIjsbbfdRjqd5sYbb+Tee+8lhHBGj21glSRJkqQzsXUrFBR07D0KCt524aVly5YxdepUSkpKACguLj6pkuPGjWPu3LnU1dVxww03MHjw4Le0qa6uprq6mhEjRgDQ0NBAbW0tZWVlDBw4kLFjxx5t+4Mf/ID+/ftz4MABbrzxRh5//HFuvfXWU33SYzglWJIkSZLORHMzpNMde49UKnefNsQYjzuSmZeXd/Qd08Y37Ok6bdo0li5dSmFhIZMnT2bZsmVt1p49e/bR91I3btzI7bffDkDXrl2Padu/f38AunXrxrRp01i5cuWpPWMbDKySJEmSdCYKCiCT6dh7ZLNvO4p75ZVXsnjxYnbv3g1AfX39MefLy8tZvXo1AEuWLDl6/MUXX2TQoEHMmDGDqqoq1qxZQ7du3Thw4MDRNpMnT2bhwoU0NDQAsHXr1jZX/21tbWXXrl0AtLS08LOf/Yxhw4adwQPnOCVYkiRJks5E//5vO/rZbpqb33aP16FDh3LPPfcwYcIE0uk0I0aMOLqwEsBdd93FTTfdxOOPP86kSZOOHn/iiSdYtGgR+fn59OvXj/vuu4/i4mLGjx/PsGHDmDJlCvPnz2f9+vWMGzcOgKKiIhYtWkT6TSPKTU1NTJ48mZaWFjKZDFdddRV/9Vd/dcaPHWKMZ1yko40aNSoeWdVKkiRJkpJi/fr1DMnLg3nzYMCAjrvRK6/AF7/Y5qJL55v169czZMiQY46FEFbHGEe9ua1TgiVJkiTpTJSW5t4xbWnpmPotLZCXlxvJfZcxsEqSJEnSmSgshAkTYPv2jqm/fXuufmFhx9RPMAOrJEmSJJ2piROhtTX3056O1JwwoX3rnicMrJIkSZJ0BmKMuWnB11zztnulnra6ulzdt1lw6XxzqmsoGVglSZIk6TR17tyZ3bt354JYVRX06wdtbPtyWnbsyNWrqmqfeudYjJHdu3fTuXPnk77GbW0kSZIk6TSVlpZSV1fHzp07AUhfdx29vvMdwp49ZIqLT7tuur6emJ/P7mnTyLz0Unt195zr3LkzpacwWmxglSRJkqTTlJ+fz3ve855jD15yCTz0ELz2Wm4qb94pxK7W1tw04IED4c476dWnT/t2+DxjYJUkSZKk9tSnD8yZA0uXws9/ngusfftCfv7bX9PSklsNuLU1985qVRWcwtTZdyoDqyRJkiS1t86d4aab4IMfhKefzv20tkKMUFCQ27c1m4XmZgghF2o//OHcasDvkAWW2oOBVZIkSZI6Smkp3HIL3HADbN2am+5bV5cLqgUFufOlpdC//7tyn9UTMbBKkiRJUkcrLISLL8796KS5rY0kSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUokA6skSZIkKZEMrJIkSZKkRDKwSpIkSZISycAqSZIkSUqkDg+sIYR0COG5EMLPDv9dHEL4ZQih9vBnz47ugyRJkiTp/HM2RlhnAuvf8Pcs4MkY42DgycN/S5IkSZJ0jA4NrCGEUuBa4LtvOHw98P3Dv38f+GhH9kGSJEmSdH7q6BHWbwJ/B2TfcKxvjHEbwOHPPh3cB0mSJEnSeajDAmsI4TpgR4xx9Wle/9chhFUhhFU7d+5s595JkiRJkpKuI0dYxwNVIYTNwA+BSSGERcD2EMKFAIc/d7R1cYzxOzHGUTHGUb179+7AbkqSJEmSkqjDAmuMcXaMsTTGWA58AlgWY/wLYCnw3w83++/ATzqqD5IkSZKk89e52Id1HvBnIYRa4M8O/y1JkiRJ0jHyzsZNYoxPAU8d/n03cOXZuK8kSZIk6fx1LkZYJUmSJEk6IQOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmRDKySJEmSpEQysEqSJEmSEsnAKkmSJElKJAOrJEmSJCmROiywhhA6hxBWhhB+H0JYF0L40uHjc0IIW0MINYd/rumoPkiSJEmSzl95HVi7CZgUY2wIIeQDvwkh/Pvhc9+IMT7QgfeWJEmSJJ3nOiywxhgj0HD4z/zDP7Gj7idJkiRJemfp0HdYQwjpEEINsAP4ZYzx/zt86vMhhDUhhIUhhJ4d2QdJkiRJ0vmpQwNrjDETY6wESoExIYRhwLeB9wKVwDbgwbauDSH8dQhhVQhh1c6dOzuym5IkSZKkBDorqwTHGPcCTwFXxxi3Hw6yWeCfgTFvc813YoyjYoyjevfufTa6KUmSJElKkI5cJbh3CKHH4d8LgauAF0IIF76h2ceAtR3VB0mSJEnS+asjVwm+EPh+CCFNLhgvjjH+LITweAihktwCTJuBz3RgHyRJkiRJ56mOXCV4DTCijePTO+qekiRJkqR3jrPyDqskSZIkSafKwCpJkiRJSiQDqyRJkiQpkQyskiRJkqREMrBKkiRJkhLJwCpJkiRJSiQDqyRJkiQpkQyskiRJkqREMrBKkiRJkhLJwCpJkiRJSiQDqyRJkiQpkQyskiRJkqREMrBKkiRJkhLJwCpJkiRJSiQDqyRJkiQpkQyskiRJkqREMrBKkiRJkhLJwCpJkiRJSiQDqyRJkiQpkQyskiRJkqREMrBK7yDZbPZcd0GSJElqNwZW6R0gm80SYySV8n/SkiRJeufwX7fSeSjGSEtLC//6r/8KQCqVIoTAhg0buP322/n+97/Pxo0bAUddJUmSdP4ysErnmZ07d5LNZnnttdf43Oc+xwsvvADAN77xDW655RauuOIKXn/9dT71qU8BOOoqSZKk85b/kpXOM2PHjuXRRx9lwIABTJs2jX/8x38E4JprruGZZ56hpKSEH/3oR/zmN7/hP//zP4HciKwkSZJ0vjGwSgkXY6S1tZVMJgPAnDlz+Id/+Aeuu+46iouLWb58Oa+99hqXXnopCxcu5KGHHmLx4sXMmzePe++992gNSZIk6XxjYJUSLJvNEkIgLy+PdDrNT3/6U8aOHUt9fT3Dhg3j2WefJS8vj4ceegiAl156ienTp9O7d2969uzJypUree6555wWLEmSpPNSOB9GXkaNGhVXrVp1rrshnRVHRlLT6TQAu3fv5rOf/Sxr1qwhm83SvXt3Bg0axObNm/nWt77F1VdfTVlZGatWrWLu3Lns2LGDAwcO0Lt3b/r27cttt91G7969CSGcy8eSJEmS3lYIYXWMcdSbjzvsIiVIJpMhnU6TTqd55ZVX+OMf/8i1117LkiVL+OQnP0n37t3p27cvo0aN4uWXXyaVSnHZZZeRSqX4p3/6J+bMmcMVV1zBRRddxL333svf//3f06dPH8OqJEmSzkuOsOod68CBA3zzm9+ke/fu3HDDDQwYMOBcd6lN3/3ud+nTpw9VVVUAPP3003zhC1/g+eef51Of+hTPPPMMmzZt4pe//CUbN27khz/8IQMHDqS5uZktW7bw+c9/nuuvv56JEyfy5JNPnuOnkSRJkk6dI6x6V4gxsm/fPr761a8ycuRIXnnlFYYMGcJLL710rrv2Fo2NjQCMGzeOSZMmEWPkq1/9Ktdeey0XXXQRU6ZM4dJLL2XUqFFcddVVzJ8/n9GjR3Pw4EEAysvLqa6u5vLLL+eHP/yhYVWSJEnvOHnnugNSe/mv//ovmpqamD17Nvn5+Tz11FP079//XHfrGOvXr+eZZ57h+uuvp6SkBIAXXniB2bNnc/PNN3Prrbfyi1/8gve+9700NTXRr18/du3aRc+ePVm4cCEXXHAB2WyWkpISCgsL+dWvfsXAgQMpKys7x08mSZIktT9HWHXeW7BgAQMGDGDChAnU1dXR0NBAc3Mzffv2pamp6eiWLtls9pz1cdGiRQwdOpSbbrqJTZs2UVNTww9+8AP69+/PF7/4RV577TX+5//8n6RSKf7mb/6G559/nlWrVvHxj3+cqVOnsm7dOrp27cqll17KyJEjmTlzJnfeeScTJkwA8B1VSZIkvSM5wqrz2oMPPsiXvvQlbrrpJr70pS/Rv39/nnnmGR5//HG2bdt2zHur52Jrl6eeeoovf/nLbNiwge7duzNixAiuuOIKWltb+cxnPsMll1zC7373OwB69OjB17/+dW677TZSqRSlpaWkUil+/OMfc9VVVzF9+nRCCHziE584688hSZIknQuOsOq883pL5PndGX6/I8PTz66mf//+fPe736V///40NTVxzz33kE6nWbhwIa2trRw6dIhMJkNzczPPPPNMh/fv1QMZ/s/vm7hn+SG+s3ofq9c8T99+F7FixQouv/xynnzySSoqKkilUvTr14+tW7cC8JGPfIQlS5YwaNAgSkpKWLp0KSNHjmTPnj18/OMf55Of/KRhVZIkSe8qjrDqvLG/KbJ4Qwv/8VIrOw5GWrNQlxnAjm2/pPKKK2mq30a3oiJ69uzJhRdeyGOPPcbtt99OaWkpAE888QTbt29n1KhRFBQUtHv/DjRn+fJvm3jm1SyZLKRDJPa7kobmDH/ck+Vb/3UAmptJpVJcdNFFfPSjH2XlypWsX7+e/v37s2DBAoqLi/n973/P4MGDufnmm/nmN79J3759272vkiRJ0vnAwKrzwp7GyL2/buS5HVm65EV6dAqkAnS/8U4Obn2BtTXLGXhpBSE0sXnzZvbs2UNDQwOf+9znGDx4MD/4wQ/Yv38/8+bN+//Zu/f4nOv/j+OPz+e6rp035rBhRpgzYWaOOZdikixCUQ4d6EBUopwyORT9hMQ3iUTIKSKJkfP5PMycZ2ZHO+86vX9/rK0WMsVmvO63mxvXZ5/r835/ru1yu557vQ/3JKymmO28uTGD0/GKEi4Ko64DWfNKazz3HmfXf82cbxehn/yN9/p2BWDcuHE0bNiQkSNHcv78eX766aecVYBHjx591/sohBBCCCFEYSP7sIr7nlKKUdsz+e2ilRJOGgb9xgWG0q12EpPSebdZUYpH7WDAgAH4+vqye/duANq1a8fixYvvWR8/3ZPBj6dtlHK9cQEkm9XC6q7FcCzqRdnunzBtSE/aVzSRmppKUFAQVquVevXqUb16dV5//fV71kchhBBCCCHuV7fah1UqrOK+dzlZsTvKhruJm4ZVAEfsOLs4szzcSsKXk0lPT+enn35i7969NGnS5J72L9VsZ/15G0WdFJp247Rwg9FEta7DiNqzhqT9q5mxujLxJc7x5Zdf4ufnR48ePWjXrt097aMQQgghhBCFkQRWcd/betlKihm8XW8eVu12O1cP/EL4qv9jS/h+inkWYeIn4zGZTDRu3Pie92/3VTtpFvB2ufXWMlWeHcKZNTMoFRjEniX/R7p7CmPHjqVNmzb3vH9CCCGEEEIUVhJYxX3vSorCoCtutai1rusUrfAoLiV8KNayH/NHvEDjMlk/2vmxP2lCurptWwYHJ8q37IFnpXoUffwtpnYsQR0vwz3vmxBCCCGEEIWZBFZx33Mxwu2mWruU9CVg0FyupYFjPm/W5JDHd1HtPpNA04hOAyd55wkhhBBCCHFbsg+ruO81LmNA0zSs9n9OrakWRTFnjfJF8rdyWaekjq6BzW7/x/M0XSfNqvBwhPIe977yK4QQQgghRGEngVXc92qVNFDBQ+d6pgJuHlrtSpFqheZlDRR3zt8wWM7DQN2SOvGZ/9yuUooks0ZQRQNORnnrCSGEEEIIcTvyqVnc9xwMGoMCTLiYNOLS1d8qrQqzTRGbrqhYROelWqYC6eNAfweMOiRl3jxQK6WITQdvF+hW/e7vAyuEEEIIIcSDSAKrKBTqeRsJaeZIWXedhEyITbcTl24nJk2RZgF/LwMTmztS0qVgfqSrFzcw8TFHdA2upiqSzXas9qxwnZCpuJoGXq7weWsnSjrL204IIYQQQoi80NTtVrO5DwQEBKh9+/YVdDfEfSDDqjh8zUroJTtpVkVJZ43Hyxvx89RvuUdrfopLt/NThJVVZywkZIAGlHHTeK6qkcfLG3F1kLAqhBBCCCHE32matl8pFXDDcQmsQtx9SikybKBr4KDnz/Y6QgghhBBCFFa3CqyyuYYQd8hms2Ew/PNKxJqm4SzvLiGEEEIIIf4TGZ8oRB5ER0fn/Ds7rM6ePZvQ0FDst9nORgghhBBCCPHvSGAV4h9YLBaWLl3Kzz//nHNs48aNNG7cmPXr1+Pp6YnFYinAHgohhBBCCPHgkkGLQtyC3W7HZDIRFBSEyWTi7NmzVKxYkb1799KjRw/efPPNgu6iEEIIIYQQDzSpsArxF39dhEzXdaKjo4mPj+ezzz5j8uTJAPz666/UrFkTgJSUlBueJ4QQQgghhLg7JLCKh158fDxbtmwBcq/ma7FY6NixIzExMTRt2pTMzEwuXbpE8+bNmT17NgBubm4AXL58Of87LoQQQgghxANOAqt4aIWFhbFgwQK2bdtGTEwMADt27GD//v1YrVZMJhPVq1dn165dBAYG4uvry6JFi3j//fcJCwvjk08+4auvvqJBgwb8/PPP2Gy2Ar4jIYQQQgghHiwSWMVD5+jRowQFBdG9e3fCwsJwdXWldOnS7Nu3jwsXLvDpp5/y3XffAVCzZk1cXFxwcHAgICCA8PBwYmNjWblyJa6uruzYsYPx48fz6quv3narGyGEEEIIIcSdkUWXxEPnf//7H02aNGH48OE5x+bNm8eMGTPYu3cvfn5+9OnThyZNmhAdHY3JZAKgevXqbNy4kW+//ZYPP/yQt956q6BuQQghhBBCiIeCVhgWiwkICFD79u0r6G6IQiohQxGeYCfdorhw8hD/N2Igv2/ZjIuLCzabDYPBQEpKCkFBQQwaNIhnnnmGTz/9lKioKJKSkoiLi2P58uUAHDhwgPLly1O8ePECvishhBBCCCEeHJqm7VdKBfz9uAwJFg+s65mKb49ZGLMjk4UnLKw4YyE0vihXM4zM2pNAXLpC17PeAm5ubrzwwgvMnDkTgMGDB/PYY4+xfPlynJycSEtLA8Df31/CqhBCCCGEEPlEAqt4ICVmKqbtz+RIjI3SruDroVHWXce3qAPlq9Zm06ZNfL4/k2spVux2OwBt2rQhMTGRTZs2YTAYeOaZZ1i9ejXz5s3DxcWlgO9ICCGEEEKIh48EVvFAWhxm4boZSrtpGPQ/t6pxK+ZFMZ9HiD22hdTUNBaetKNpGjExMZw9e5bOnTuzdOnSnPObNm2Kg4NDQdyCEEIIIYQQDz1ZdEk8cKJT7YTF2SnrfuPXNE2jXrturP9yFL9N7k/Nru/xxZFwFn89k4CAACZNmoSTk1P+d1oIIYQQQghxAwms4oFzLNaOrmWF05spUrIMTw+ezLYfZnDghymctiUz8eOPadOmTT73VAghhBBCCPFPJLCKB05ipsJ0my1Rnd2L8ni/EVyNS6ZWWQ/a1JJhv0IIIYQQQtxvZA6reOA4GcBqz9t2TbqzG07Gm1dihRBCCCGEEAVLAqt44FQtZsBmz1sINdugdgl5GwghhBBCCHE/kk/q4oFTsaiGl4vG9czcVdaM1GTsNhsASilSLQo3E1QtJm8DIYQQQggh7kfySV08cHRNo3t1EylmSDZnhdbocyc5sG4RusFAWlIC6VaIz4Ae1U0YdRkSLIQQQgghxP1IAqt4IFUsqvPqowYsNsWlZDumUlXZvXoeX77Rkc9eaMT5U8foX9tIjRK3WZ1J/Ce2PyraQgghhBBC/BuySrB44NhsNgwGA9VKmBjd1MjhK2nsupxJ0RKlSI69wqK1obR+tLwstnQPKaXQNA2DIesXAna7HV3Xc44LIYQQQgiRF1JhFQ+c7JC0fft2gp5qx/xJw+jkk8LudT9Qo7w3RdIj0e2WAu7lg0mprCHY2aH0p59+omvXrkyePJmYmBgJq0IIIYQQ4o5IYBWFmlLqhmGnUVFRdO7cmcWLF/Pqq69is9n4/PPPMRgMtGnThoULF6Lr8qN/N2V/D7IDqdVqZeTIkXzzzTcMHjyYs2fPMM9TXwAAIABJREFU8tlnnxEVFQX8GWyFEEIIIYT4J/KpXRRqfx12eujQIQCKFSvGkSNHcHd3p0uXLvTp0weDwcCaNWvo06cPJ06cYO3atYwYMYKwsLCC7H6hdvr0aSArfGZ/D7788kumTp2Kruv06tWL+fPnc/jwYTZs2MDRo0fZsGEDgFRahRBCCCFEnkhgFYWK3W7HbrfnPLbZbEybNo1q1aoxefJkxo0bR3JyMiNHjmTLli0A+Pv788gjj7B3715cXFz44IMPWLJkCeHh4RQvXrygbuVf27VrV0F3gUOHDjFr1iwgK3weP36c1q1bs3nzZpo0aYLdbqdSpUrMmTOHw4cPs3XrVgICAti7dy8xMTEF3HshhBBCCFFYSGAVhYZSCl3X0XWdtLQ0AMLDwzl79iwnT55k4MCBzJw5k7Vr19KxY0fsdjsbNmxA13Xq1KnDtWvXWLduHe3atWPevHksWbIELy+vAr6rO7N7924+/PBDIiIiCqT9mjVrcujQIerWrcv48ePZs2cPkBVgK1SowJIlS2jYsCFGoxFN01i0aBFBQUH4+vqSmJjI4cOHc36RIIQQQgghxO1IYBX3texqavbqsuHh4bz00kt0796d+Ph4YmNjOXnyJJ07d2bo0KF8+OGH9O7dm2LFitGzZ08mTpwIQGBgIG+88QadOnUCwGQyFdg93YnsCvKmTZuArNehUqVKOYE9P9jtdqxWKwCtWrVi7NixAPzyyy8EBwcDcPnyZapUqYLZbMZsNuec36NHD8aOHUvnzp05f/48Y8aMoXPnzvnWdyGEEEIIUbjJtjbivpS9DUr24kiappGUlMSAAQNo1aoVAwcOpEiRImRkZGA2m3niiSd45513AEhNTSUhIYHg4GA2bNhATEwMJUuWJCAgoCBv6Y5YLBZMJhPh4eHMmjULPz8/AgMDqVevHkOHDiU9PR3gnm4T89fvQfb3Yfz48ZQtW5bIyEg6derEjBkzWLt2LTVr1mTVqlVcunSJSpUqAXDw4EEGDRqEr68vKSkp9O7d+570UwghhBBCPLikwiruS9kBad26dXz00Udcu3aNzZs34+Pjw/DhwylSpAgA9erVw9/fn/DwcGJiYli4cCHNmzdn165dlCpVitWrV1OyZMmCvJU7cu7cOebPn8/rr78OQNWqValUqRJFixZlyZIl2Gw2AgICmDdvHnD3V9sNCwvjm2++ITY2Nud7sGbNGqZOncrp06fx8PDgueeeY8SIEQD07NmTzz//nKCgINzd3Rk6dCifffYZjRo1YtGiRaSmptKlSxcJq0IIIYQQ4l+RwCoKXExMDEFBQezfvz9nCHB8fDx9+vRh/Pjx1KxZEy8vL7y9vdm+fTvwZ1ArXrw4b731FqVKlaJv374sWLCASZMm5QxVLSx+//13unbtSnBwMK6urvz+++98//33aJpG27ZtMRgMeHh48NFHH/H8888TGRmJ2Wy+a9vzHD16lKCgILp37054eDiHDh3i3Llz9O3blzlz5gDw3nvvcfDgQUJCQli+fDmpqam8+OKLZGRk8OuvvzJlyhR69+5NdHQ0Y8eOZdKkSbi6ut6V/gkhhBBCiIeTBFZR4C5cuMDvv//O5MmT+frrr3OOXbx4kd9//53nn38egEaNGlGqVCm++OILNE0jOTmZuXPnUqpUKUaNGsW3337L+vXradOmTUHezh3btGkTH3zwAU899RSbN2+mS5cujBo1itmzZ3Pw4EGaNWuGt7c37du35/Tp06xZs4bq1atz9uzZf91msllxMs7GkRgbp+PtfDl7Dk2aNOHQoUOMHz+etm3bopTCy8uLVatWoes6Bw4c4OzZs5QqVYqWLVvy8ccfo+s6HTp0ICQkBIBnnnmGSZMm8cQTT9ytl0cIIYQQQjzEZA6rKHAmk4lKlSoxZMgQgoODadKkCbGxsVSsWJHIyEhKly6NzWbDZDIxZswYJkyYwLZt2zh16hRNmzale/fuAHh6ehbwndyeXSnOXVdsj7RxKSmrmhweGkZZvxr0fukldE3DZrPRo0cPTp48yaJFi/D29qZ8+fI4Ojry9ttvM3bsWK5cucKAAQPuuP0rKXZCL9rYF20DFKARffoQmzbuZOrijcSlK4o62DEYDJw7d45ffvmFlStX0r59e3bu3ImPjw8AISEh1KlThwkTJjB48GB69ep1F18lIYQQQgghskhgFfnqeqbiQLSNIzF2LHZFeQ+dRhVqo5TKqZSuWLGCqKgofHx8OHbsGD4+Pui6ztGjR2nRogXNmzdn3bp1NGnSpFDto5qQofjmqJmLyQoHA7ibAA2SnUtx4OQaOgwYSynrFYoXccfT05P69esTGxvLp59+SuPGjenfvz9t27YlMjKSQYMGER0dTbly5fLc/vFYG18ftWDQwMsFjH8MJ3bxKYmjowOhZ5M4fN2RVx818kjRrF8AVK9ena5du+asrvzrr7/i7u5Oo0aNmDlzJhkZGTg6OlKmTJl78ZIJIYQQQoiHnHa3F225FwICAtS+ffsKuhviPzp8zcqCE1asdvBwAF2DFDMkxkRxdd0X/G/iB8TFXOOVV14hMjKSHj16sGvXLp577jnWr1+P0WhkwoQJVK5cuaBv5Y4lmxWf788kKRO8XXOv6mu1mNm1fA6HNq+hQr2m1PU0kxATTUJCAnPmzKFbt25s2bKFVatW0bFjRzIyMjCZTBgMhjy3fyHJzuf7zHg6gYspd/vJcdH8+vV4KtZrRoXmwZjNVt5r7EpxZ42RI0eyYcMGunfvztatWwkPD+fTTz+VIb9CCCGEEOKu0jRtv1Lqhm09pMIq8sXZRDtzj1op4QLOxj8Dk5sDFHcsydq9O3msbXv0zCR69epFWloaERERVK1alZSUFNq1a/evhsDeLzactxKfAT5uN25BYzQ50KzbQJp1G8ilBDONyjnR2CWKQYMGATB8+HBcXV1zhjw7OTndcftrIqw4GW8MqwBuxbwoVuYRzuwLpcZjHUjVnNlyyUo7HzMVK1bko48+4uDBg7Rr144ff/zxjtsWQgghhBDi35IKq8gXMw6auZxsp7jzzfcMXTF5MFEXz3Foy894OGpYLBZWrlxJpUqV8Pf3z+fe3l1pFsXIbZkUdwGTfus9U+12O3Y0YtKg5L4ZHD6wj0WLFv3n9q+l2QnZaaasO7fcs/V6zBXWfzkKc3oazXu9y9nTYcRvmkNAfX+mTJmCyWT6z/0QQgghhBDiVqTCKgpMfIYiPMGOj9utzynqXRajVyWOx9kI9NYwmUw899xz+dfJeygi0Y5N/XNYVUoRc/4UhzYu5XDoWmpWKsfEsR/mfO1WQTMvziTYQbt1WAUoUrIMTw+ezLYfZrBt4RQSr1/no/dH0aezDP0VQgghhBAFRwKruOdSzQr9NoHJ0cUdu+5CfLodg8EhH3t372XYbn+Opml4eJXBo0Rp2g6dxdsdGxBYxpDztf8i1aLQ/1gR+J84uxfl8X4jyExLIdbmin8tqaoKIYQQQoiCJYFV3HOORlDqnyuFjbv0JzIF3B0evK2B83pLzm5FaPxsfy4lK0x5X0/p9tc1athV3kOvo4sbJCuMD963QgghhBBCFDLykVTccyWdNbxdNZLNtz4neyZ1teIP3o9kOY+s+qbNfvv54nal0IDyHnfvdfD10NG0rF8Y3LRNuz3X4+x+lnV/8L4XQgghhBCicJFPpOKe0zSNJysYiM8A601Cm1KKqynwaEkdL5cH70fS00mjbkmd2PTbnxubDrVK6LdcnOrfKOeuUfomvzDIDqq6/udrrpQiLgPqeukUdbx7fRBCCCGEEOLfePDSgbgv1SlpIKiSgSspcC1NYbMrlFIkZSouJSvKFdF4vtqDO2fyqYpGdA2uZ/4Z2P9e2UzKVCgFT5a/u0FR0zTaVzSQkAEWm8ppOzuonju0ne+Gv0BKQgwZNjDboHU5mS0ghBBCCCEKngRWkS80TaPdI0YG1XegVgkD19LgSiq4O2j0rmliQF2Hm+4R+qDwdtUZWM+BtOvx/LLkW1LMf87nTbUoLicrbHYYWM+BskWygvvPP//MmTNn7kr7tUoYeLqSxpUUSP6j7aTYKDZ+/Qmb53+Gf/ueWJxLEJOm6F3TiO9dHJIshBBCCCHEvyVlFJFvNE2jYlGNikV1VA1jzrGHgd1up5yHztgnSlN7yDT0tDiuXrnIIw2fol6ztgRXcaCulwE3B43ffvuNIUOGULlyZapUqYKnpydDhw79T+0rpXi8giPerha+3niUsPhk9swPITUuCqs5E/c67SjupOhT25FKRSWsCiGEEEKI+4MEVlEgHpagmi17+O350yeo4fcIG78ew+z/fU23bh1wNBmwWCxYrRnY7Y7s3r2b1atX4+npSXBwMA4ODvTq1QsvL688t6eUylmVWdM0rFYrISEhLF26lHLlynHw8BHeGjeDyEsX+XrCMFqr/Twd0PRe3b4QQgghhBD/ipRShLjLlFI3zE/dsGEDI0aMYPDgwQwbNowiRYrQuGEgCXExTJ8+nVatWrFp0yZ0XWfTpk1069aNdu3a0aRJE5YsWZLnsKqUIjk5mfj4eHRdJykpCYDFixezbNkyWrVqxYABA3B2dODyvg0c2bgUXYPJo4ahadoN/RZCCCGEEKIgSYVViLsoezGjv1aQExMT6du3Lx07dmTDhg0ADBgwgGeeeQaAV155BZvNxuzZs2natCktWrRg9+7drFmzJucaoaGhBAQE4Obm9o/tZ2RksGPHDo4cOYKPjw9Tp07lscceIywsjJdeeomIiAgmTJhAYGAgCxYs4MKFC3z//fe88847HD9+nJo1a96DV0UIIYQQQoh/RyqsQvxHf93fVNd10tLSGDlyJO+++y4bNmygaNGivPzyyxw+fJjk5GQARowYQXh4ON27d6dv375MmjSJmJgYTp06Rdu2bUlOTmbevHmsXr2aFi1asGjRIqxW603bt9vtOZVRZ2dnvv76az788EMGDBhAbGwskZGRaJrG5cuXsdvthIaGUq9ePQwGA+vXr6dcuXLYbDa++eabe/9iCSGEEEIIcQcksArxL9nt9px5otmSkpJ49dVXuXbtGjVq1GD48OGsWLGCRx55hCNHjvDiiy/y7bffYjQaqV27NgsWLKB+/frExcVRvXp1Zs2aRePGjRk1ahRnz55l+vTpDBo0iK+++oqiRYvmaju7fV3XOXXqFIcOHSIjI4MTJ07g4+ND//79mTZtGmlpaVStWpWLFy+SkpJCamoqZ86coWjRogwdOpRly5axePFiRowYURAvoxBCCCGEELckQ4KF+JeyF1IKCwtj2bJldOvWDScnJ44ePcqhQ4cAuHz5Mj/++CMxMTH06NGDq1evMmHCBBo0aEC/fv3YunUro0ePpmrVqly/fp0xY8Zw5swZWrduTatWrXKF4b/unZr9N8Dp06dp3749uq7z/vvv4+zsTK9evTh//jzFihWjdOnSJCcnU7p0aaKjo2nYsCGPPvoo8+bN4+TJk/Tp0wdHR0eZvyqEEEIIIe472l+HM96vAgIC1L59+wq6G+IhZ7PZMBgMOY/tdjujRo3i559/pmzZsgQHB9OgQQOmTJlCYGAgq1evxsfHh1OnTjF9+nR++OEHxo8fT0BAAN7e3vz4448MHDiQjIwMSpQowbVr1wgLC+PFF1/krbfeygmoVqsVTdNQSmE0Zv2OKTIykmHDhuHm5oaDgwNRUVE4ODhw+vRpatWqhdVqpVixYvj5+bF7926ioqKoUqUKxYsXp0ePHlSvXj3Xffw1AAshhBBCCJHfNE3br5QK+PvxPFVYNU179iaHrwNHlVLX/mvnhLhfpaSk5Cx0lB1WDxw4gJ+fH0uXLmXt2rUsWrQILy8vihQpQlRUFFFRUXz88ccMGDCA999/n5YtW3Ls2DGOHTvG2rVrqV+/Pv7+/hw/fpxZs2YxZ84cEhMTmThxIp9//jmOjo4AbN++HU9PT3x9fSlSpAjJyclEREQQGxuLr68vrq6unDhxArPZzJQpU1i4cCGNGzcmNDSU9PR0AgICWL9+PR4eHjg4OODh4UG3bt2oXr16zrY3uq5LWBVCCCGEEPetvH5S7Qv8D+j5x585wDvAdk3TXrxHfROiwOzYsYMePXowY8YMLBYLACtWrKB+/fp88sknxMXFMWrUKI4ePUp8fDxOTk689dZbrF27lvfee4/MzEyWLVtGjRo18Pf358iRI1SvXp0nnniCnTt34u7uzqRJkwDo168fjRo1omfPnixcuJDz589TpUoVnn/+eZ5++mm2bt3K1KlT8fPzo23btnTv3p05c+ZgMBhwdnYGoEiRIri4uHD16lUcHR0ZNGgQ9erVo0qVKri5ueHi4kLfvn2pVasWkLUPrgRVIYQQQghxv8vrJ1Y7UF0p1UUp1QWoAWQCDYH371XnhMhvoaGhtGnThpEjR9KoUSPef/99TCYTJ0+e5KOPPmLChAksXboUb29vevXqhZOTE+PGjcPZ2Zm6deuya9cuqlevzquvvkqlSpWYPHkyU6ZMoXnz5ly8eJEGDRowc+ZM5syZwzfffENUip2T8XaWb9hK08eDePHFF9myZQvu7u6EhIRw8OBBGjZsyNq1a3njjTfYtm0bzZs3Z+vWrWzZsoUqVapgt9tZsWIFXl5enD17ljp16jB37lzefPNNBg0ahMFgwNPTk2LFihX0yyuEEEIIIcQdyeuiS48opaL/8vgaUEUpFa9pmuUe9EuIfGO2Kc4m2km1QFh0KmcizhIecZawOBtfHjJz9tw50uOvcfz4cQYPGUq/Pi+TkpLCnj17ePvtt/nqq68YOnQoiYmJrF69mrp16/Lyyy8zcuTInOG9TzzxBHXr1mX37t0EBQVxJMbGF4dtnE8wY9V04hoM5mhsDMfm96FuQEtK2GLp3bs3mqaxd+9eihYtyrJly9i/fz+nTp1C13UWL15McnIya9euZdKkSQQGBjJp0iRatGhBVFQUZ8+e5bfffmPIkCE8+eSTBfwqCyGEEEIIcefyGlh/1zRtDbD0j8fBwFZN01yBxHvSMyHuMatdsfmilU0XbaRnb3FaojWxqWaCv79E2vHfOLZ8GjWad6T5y8Px8C7P5fhUfvxtFwGVy/D7778TEBBA37592bhxI0FBQbz77rtERERQvnx5SpQowebNm6lfvz6enp54eXnRvkMQS09Z2XrRgruTRpJF43KKHQ0wuniQnnydU5aSHDh6gGfeGENJSxTmjHSuXLmCr68vSUlJhIWF4efnh67rZGZmsnjxYkaPHs0HH3xAs2bNAPDx8cHHx4eQkJACe32FEEIIIYT4r/K0SrCWtbfGs0AzQAO2AT+qfFpiWFYJFneb1a5YcNzCwWs2vF00HI1Z28ckZipWLl3M8S/6UK5Nb9p0ep4aDVsCEL5nExv+F0LdniM4teQTPE02goODady4MePGjWPatGlkZmYyevRo+vXrR5UqVUhPT6datWo57a6JsPDLOSs+7nA0RhGdpnA3Zc0ptaQnE7ZwDG5lq5KWYSbhwM/4N2pG7aJmNm38lUuXLqFpGj179uTgwYM4OjpSpkwZvvjii5yFoWTFXyGEEEIIURj9p1WClVJK07TtgJWs+ax7bxdWNU1zArYCjn+0s0wpNUrTtGLAD8AjwHmgq1Iq4Q7uRYj/bEekjQPRNsp5aDl7nSqlOBpjx7fJs5ya8xbVXplOquOf+6BWrN+C6A96sHfuCBKuXuGSNZ3nn3+ewMBAatSowfTp05kyZQq9evWiVq1alC5dOleb1zMVv12w4eOucS1NcTVV4eFATvsmZ3dcS1ci+WIYVZ//gMynXqecm50najpToXw5li5dSnR0NL/88gvOzs688MIL9OvXL1cbElaFEEIIIcSDJK/b2vQDRgKbyKqwfqFp2lil1Nx/eFom0FoplaJpmgnYpmnaOrIqtb8ppSZomjYMGIYs3CTykc2u2HjBSkmXP8MqQGImpFoUHo5Gqj73PheWjMH23ChSzIozoUvJSE3i8f4fErFvCz2/3EHstu+pWNEbXdfp0KEDx44dw26306FDh5u2u/+qDQCDBueuK5wM5GofwKdJZ07+8AnH5g6j/PMfk2TRGf7OJFLDtlC7dm3WrVuXszKwEEIIIYQQD7q8zmF9F6inlIoD0DStOLADuGVg/aMCm/LHQ9MffxTQCWj5x/FvgVAksIp8dDlFkWwGH/fcYTE61Z6zbHalDq+zrq8fuHvz1e5lFC9RkrZ9P6B05Uf57ZuJeGjpmBv3oE3zrEWVWrRoQYsWLXKupZS6IYzuj7ZRxBHSrZBszhoK/HdOnqWo8cJoDs9+h9P/e5Pdp/diNBgIDPBn6NChODs759pDVQghhBBCiAdZXgPrZSD5L4+TgUu3e5KmaQZgP+AHzFBK7dY0zVspFQWglIrSNM3rDvssxH+SblFomiJrsMCfzDbQ/zhkcHCiwhN9iD+yiaZv/B9NA+rknNekyytsW/wF1YLfI9MGjgaVa1ixpmk3hFWANCsY9T/budk5AA5unjR45xsSL50i6vAW/Bs0YkRwA6p5Zp1/q+sLIYQQQgjxoMlrYI0EdmuatuqPx08DezRNewdAKTXlZk9SStmAupqmFQVWaJpWK68d0zTtFeAVgHLlyuX1aULclqNRQ6kbA59JB/tfZmbXeGEMKRYo5Zm7ktm273A0XScyBRz+Nqz3n4KkkwEybGDQIS/Llbn7VMG5TBVKFAGTJvNThRBCCCHEwyevn4AjgJVkLbhkB1YBVwD3P/78I6VUIllDf58EojVNKw3wx9/XbvGc2UqpAKVUQMmSJfPYTSFur6ybhosJMqy5U2NJVx37Xx5rWlawLOmcO4TqBgMJGVC5qIazMe+VzjpeOtczwcUILiYNs+2fU2u6DUo4azibDJQrImFVCCGEEEI8fPL6KfhnoC7QGXiOrH1Yg5VSY5RSY272BE3TSv5RWUXTNGegLXASWA30/uO03mSFXyHyjcmg0bKcgWtpWUN4sxVzAifDn0EyzQJFnDQ8HHKHUqUUKRZoVT6vAxSyNChlxK6yJnJXKKKRbs3dfm4Kux0cDRrNyhpwNMgQYCGEEEII8fDJ6yfu74ChwDHIVYT6J6WBb/+Yx6oDS5RSazRN2wks0TStL3CRrAAsRL5qUdbI6TjF6UQ7pV0VRl1D1zRqltDYf1WRbrXjaNB4tKSea6qrUorLyVDXS6eq551VPYs7azQpY2BbpI0yrnDFWSMhQ+Fm+vsCTYrrmQpXk84jHjqtyt1ZMBZCCCGEEOJBkddPwjFKqZ/u5MJKqSNAvZscjwPa3Mm1hLjbHAwafR81sSbCys4rNqz2rIWYlNIo46YRl6Hh666hFFjsCruCxAww26Get0736iYM+p1XPTtXNpJiURyMtlGlmMbZRLiWptA1hYOeteXOdTO4O2g0L2ugXx0HXE1SXRVCCCGEEA8n7dZDEv9ykqa1AboDv5G1vyoASqnl965rfwoICFD79u3Lj6bEQyjVojgVbyc5U+Fo1KhQRMPNQeNQtI3fL9tIzFSYdKhRwkBTHwO+7v9tlV6rXbHrio2NF6wkZCjSrBCTpkgxKxwMUL+UgRdqmKjiqctqwEIIIYQQ4qGgadp+pVTA34/ntcL6MlCNrL1Us4cEKyBfAqsQ95KrScPf23DD8aZljTQte/eH4xp1jWZljTQuY+DcdUVipkIBRRw0KhbVMP6Lyu3dZDabcXBwKNA+CCGEEEIIAXkPrHWUUrXvaU+EeMgYdA0/z/ungmq1WjEajTlh9fLly5QtW7aAeyWEEEIIIR5meV01ZpemaTXuaU+EEAXKaMz6/VX//v1xcnKiUaNGzJ49u4B7JYQQQgghHmZ5DazNgEOapp3SNO2IpmlHNU07ci87JoS4d6xWK1arNdexRYsWERAQwIYNG9iyZQv169dnyJAhN5wnhBBCCCFEfslrYH0SqAw8AXQEgv74WwhRCBmNRoxGI0eOHGHmzJkAVKxYkaioKEwmEw0bNmTVqlUopQgJCSng3gohhBBCiIdVngKrUurCzf7c684JIe4Os9mc63FSUhKNGzcmMDCQiRMn4unpSfny5WnXrh3Fixdn3bp1ADzxxBPMmTMHuz2v2y8LIYQQQghx9+S1wiqEKISyh/P+fdXfS5cucfHiRbZt28aFCxdwc3Oja9eu9OnTh/T0dNasWQPA/PnziYqKYsGCBfnedyGEEEIIISSwCvGAOX78OF999RXw50JK48aNo3jx4jzzzDOsWrWK7du3o2kaVapUAbLmr+7cuZO6detSuXJlduzYQWhoKG5ubqxatYrevXsX2P0IIYQQQoiHlwRWIR4gZrOZd999l+nTpxMWFgZAv379mDVrFlOnTsXBwYGuXbvSqlUrYmNjOXr0KADNmjXDzc2NrVu30q5dO/z8/ChdujQAQUFBBXY/QgghhBDi4SaBVYhC7rPPPqN169bs3r0bBwcH2rdvj4ODA3PnzgUgIiKCESNGsGLFCn7++WcqVKhA5cqVqVWrFi+99BIjRoygZcuWODs70759e1555RWWLl1K1apVC/jOhBBCCCHEw04CqxCFjNlqZ/05C+N2ZvDR7xlsPZvM5s2b6dmzJ7/88guvvfYa3t7e7NixA8gKrAMHDiQ1NZX169dz8uRJANavX09wcDBz584lPj6exYsXF+RtCSGEEEIIcQNNKVXQfbitgIAAtW/fvoLuhhAFbnW4mRmHLCSZQUeha2CxKXb3LkqpagGQeJl3hw7BZDIxd+5c3njjDeLj4xkxYkTOSsE//vgjo0aNYt68eQQEBJCSkoKbm1sB35kQQgghhHiYaZq2XykVcMNxCaxCFA5LTmYyZb+FIiZwdcg9OOLgrLeJObWPmh36EPXTp3iVLEn58uVJSEhg06ZNVK1aFYvFQnJyMikpKXTs2JHvv/8eXdfRdRloIYQQQgghCtatAquxIDojhLjmvcEHAAAgAElEQVQzUSl2vjhopZgjOBlvDJi1+0zkp24lsD4aTPdmTfjqtTacOXMGBwcHVq1axalTp1i1ahV79uwhJCSkAO5ACCGEEEKIOyelFSEKgRXhFmz2m4dVAKODE97+TxAxuSP79Wp8/c23VK1alZiYGNatWwdAp06dJKwKIYQQQohCRQKrEIXAxgs23Ez/PHw/YPBcrp85QLoFSgd2YPfu3SxevJhZs2b9qzaVUthstpx/CyGEEEIIkd8ksApRCKRaFKbbvFsd3IpS5JFaXFg2liRzVsDs1q3bv25T0zQMBgPp6ek5CzYJIYQQQgiRn2QOqxCFgItJI9WscLzNeS0nbyMm04CHg3bHbSil0LQ/n5eQkMCYMWPYunUrnTt3plKlSvTo0eOOryuEEEIIIcS/JRVWIQqBNuUMpFhuH0LN6DibwN/7zt/a2WHVYrEAsHz5cvz8/AgNDUXXdcaPH098fPwdX1cIIYQQQoh/SwKrEIVAlyomDDpkWO23PEcpO3EZGkEVjbdcnOnWz1VkZmYyY8YMli1bBmQF1oyMDPr378/GjRsJCQmhWLFi/+k+smXPjRVCCCGEEOKfSGAVohAo7abzZj0j8ZmQar4xtNrsdq6mafgV1en3qOkfr/X3sHjw4EEsFguOjo5ER0dz5MgR4uPjqVevHlOmTGHIkCFs3ryZTp06ER4ejtVq/c/3YzAYAO7KtYQQQgghxINLAqsQhUTXao4MD3RAaRpRqYroVDsxaXaiUhWx6RpNyhiY2dYRN4ebv62zg2p2WExKSiItLY1Ro0YxZ84cAJ599lni4uLYunUrTz/9NGXLlqV06dJYrVY+++wzJk6cyJUrV+6473Z77pC9cOFCGjZsyOjRo1m5cuVNzxFCCCGEEEICqxCFyNOVHVjT2ZlRTRx4qqKRVuWMvFTTyOKOznzWyokiTrnf0sePH8+pYmYH1d9++41nn32Wbt264eLiQnBwMFu2bAGgbt26lC5dmr1791KvXj2effZZhg0bRv369dm/fz9vvvkm5cqVy3N/s7fD0XUdm81GREQEP/zwAytWrODbb78lICCAN998M+ccIYQQQggh/kpWCRaikHEw6jxZQefJCv889Pfw4cPs2rULX19fPDw8OH/+PB07dqR58+ZUq1aN6dOnc/nyZZ566inWr1/PkiVL6Nq1K7Vr1+aDDz6gefPmDBs2jLS0NBISEvDx8clT/5RSKKXQdR1N0zCbzaxbt45p06bRvn17+vTpQ4cOHZg0aRK//voraWlphISEMGLECGw2W06wFkIIIYQQQgKrEA+QK1eukJaWhp+fH3Xq1KF27dqEhobSunVrTp8+Tdu2bZk6dSoAUVFRDB8+nPnz59OyZUsmTpxIrVq12L17N/7+/ri6uqKUwtnZGRcXl5whu7eqhGZXUzVNQ9M0kpKS8PDwYNKkSaxbt47Ro0fz+OOPAzB37lxOnz7Nzp07WbNmDf3792fEiBESVoUQQgghRC4SWIV4QFgsFk6ePMnOnTt5+umnuXz5MhkZGSxatIjixYtz9epVTp48mXP+hx9+SL169UhMTOSVV14hPDycAQMG8PTTT/PJJ59gNOb+7+F2Q3azt8U5ffo0+/bt46uvvmLatGk8/fTThIaGEhsbC0BqaiqnT5+mSZMmAMTExBAdHc2yZcsIDg6+my+JEEIIIYQo5GTSmBCF2F9X/DWZTFy9epWpU6cSFBSE3W6nQYMG1K1bl7Vr19K9e3d27drFmTNncp7r4eHBJ598AsCYMWMIDQ3lnXfewWg03nYRpJttTTNu3Dg6depEWFgYKSkpLFmyhEcffZRGjRoRGRlJamoqrq6u+Pr6cuTIEWrXrs2hQ4fYuHGjhFUhhBBCCHEDqbAKUYhlD6FdvXo1qamp+Pv706hRI2rWrEmHDh0AqFOnDsuWLSMhIYH333+fYcOGUaVKFSIjIxk8eDATJ05k/PjxuLi4AOTMI71dRTW77ejoaLy9vUlISGDbtm3s2bMHd3d3AgMD+eqrr4iIiKBNmzasWrWK/fv307x5c1555RUiIiI4c+YMQUFB9/AVEkIIIYQQhZlUWIUoxM6cOUNgYCAzZ87EYrFQrVo1PvzwQ65fv87GjRsBqF27NuXKlWP+/PkMGzaM4cOHk5KSwosvvsiQIUPw9fXl448/zrnmzeaRZmRk5Pw7e67qvHnzaN26NUOGDGHJkiW4u7tz/fp1du3aBUDDhg3x8fHhhx9+oFmzZpjNZnbu3InNZsNkMlGtWrWcsCpb2gghhBBCiJuRCqsQhYDFYsFk+nNV4G+//ZaWLVuybds2evbsydtvv53ztSpVqlCmTBm2b99O27ZtKVOmDH5+fmzatImLFy/i7++Pv79/zvk//vgjHh4et2z73LlzrFixgnfeeQelFJqm5cyVXbhwIdu3b2fSpEkA9OrViyVLlvD444/j5eVFWloaoaGhvP766/Tr1w8vL6+bBmLZ0kYIIYQQQtyMll0tuZ8FBASoffv2FXQ3hMgX2aFwwYIFnD17llGjRuU6DtClSxeCgoLw8vJi5MiRlC1blkqVKhEaGsrChQu5dOkS8+bNIy4ujnr16vHCCy/g6+tLkSJFctqx2+3oup7run/9WvZqvwBVq1alQYMGHD16lHnz5nHkyBF+/fVXateuzcKFCwkODmbIkCFcv36d5s2bM2DAAC5cuEB6ejpms5ng4GAZ+iuEEEIIIW5J07T9SqmAvx+XCqsQ95nskDhq1CgyMjJ46qmnSE5O5rvvvmPatGm4u7vTtGlTdF2nQ4cOeHt7YzAYKFWqFImJiXz33XeEhISg6zo7duzgzTffxNPTE/gz9CZmKmLSwGyz4WDQKO2qcHPQc4JqdsXTYrFgtVopV64cW7ZsYceOHfj6+hITE8OWLVvw8/Pj4MGDGAwGMjMz8fLyYuXKlaxbt46KFSvy4osv8sYbb1C1atVc7Yv7l+yFK4QQQoj7iQRWIQqIUorIFEVChiI1NZUtPy2mS/s2+FWqRFpaGs8//zwREREsWrSI8ePHs3r1at577z0mTpzI1atXcXBwACAgIACz2UxERAQpKSkULVoUgLZt29K2bductgDOXldsu2zh0LU/54xa01PJTImnbZ1HaOJjoKy7Rnh4OJMmTcLb25v333+f5cuXExgYSEJCAr6+vnh5eREUFESJEiUwGAxs2rSJ2bNnM3DgQB577DH8/PzYuXMn3bp1o1ixYhQrVgxAwmohkB1Ws4ehyy8ZhBBCCFGQZEiwEPnMrhSHom38esFGVKpCA66dOcT3rzWhflBvps2Yhb+Xxssv9aZTp06sXLmSd999l/r16zN48GCcnJwoXrw4x48f55tvvkEpRf/+/Tl58iQvvPACr732Wq72lFLYFaw5a+W3CzacjFDcCQx6Vgi5eHwvS8e9Ts85e0lKiCFzyyzSo8Jp1qwZGzdupHbt2oSEhDBu3DgOHTrEsmXLANi3bx/vvfceuq6TkZFB79696d+/PwAnTpxg4cKFPPbYYzz55JP5+vqKvIuIiMhZxTk7qC5YsIDly5fj5+fHa6+9RqVKlQq4l0IIIYR4GNxqSLAEViHykc2uWHLKyo5IG55O4O7wZ9VxSs9AXIqVomzgUzzd6Rky9/5ActJ1fH19OXr0KDNnziQlJYVJkyaxaNEigoODc4b+XrhwgfLly+e089eqmFKK1Wes/HrBiq+7lhNU/2pil1r0mboSo2sRvhrYgbZtWrN49lT27NnD119/Tbdu3WjYsCG1atVixYoVHDp0iMcee4xKlSpx7NgxatWqlT8voLgrsucv//DDD8yaNYuFCxdSpkwZNm7cyMSJE/noo49YunQpFouFHj160Lx584LushBCCCEecLcKrLI0pxD5aP05Kzsirfh6gIejlmuoZce3J2DPTKVqtWpMG/4aca4VqVy5MvXq1ePy5cscO3YMNzc3xo4dS5cuXThw4AC6njXvNDus2mw2IPfQ24hExW8XbbcMqwCdhnzG+i9H4Vm8JI2f6c2eo6eJTbMTGBiIl5cXO3bswNXVlbFjxzJy5EjmzZuH2WwGyAmr2W2L+5vNZsuZo9y1a1fc3d3ZsmULAGvWrKF+/fo0b96c4cOHU6dOHX755ZeC7K4QQgghHnISWIXIJ8nmrODo466h32ROoF+DVly7cJrytQJ5pEoNZo4cyNX4ZAIDA6levTpffvllzrkDBw7E3d0ds9mca0uYmy2W8/tlK85GbhlWAao2foKzB38nJSGGuq2fweDgwLTvVgHQsmVLDh8+zObNm3nxxReZP38+oaGhVK9ePdc1ZKGewsFgMJCcnMzkyZP5v//7P9q2bcuqVatITEwkMDCQ5ORkbDYbpUuXBsBsNufah1cIIYQQIj9JYBUinxyItmFXYLxFcNQ0jQ5vhrD+y9EEvfUJzV/7hMyi5dF1nfbt21OzZk2sVitxcXG89tpr+Pn55Sy8dCsJGYojMXaKO/9z37LaHs+G2R9TxMuHqv5NWbZkMZk2RZs2bejRowcBAVkjNLIXdZKK6v3PZrPx92kfmzZtonLlytjtdl5++WXeeust4uLi2LVrFz4+PpjNZhYtWgRASkoKRqMRJyengui+EEIIIYQEViHyy5EYO+7/nC/xf7IHB35ZjCUjncYde+JUK2vBohYtWjBgwACMRiNKKd58800mTJhw2zajU+1omrppRffv6rV7niObVmDJzKB648exWq0cOXUOgM6dO+Pu7p7rfKmo3p8yMzNz/m0wGNA0jSNHjnDixAkAWrduTUpKCsWKFcvZl7d9+/asXLmSmjVr0qFDB6ZPn87jjz/O0qVL6dixY4HchxBCCCEESGAVIt9kWBXG27zjDEYjTbq8wtZF0zBqkG5VuSpkSilKlCiRa+Xd7ErnzRZQM9tBqbxtSWIwGmncpT/bfpiBd4VqdBj5HaXKPpKrbXH/CgsL45VXXmHgwIHs3r0bgO3bt9OpUydeffVVxo4dy//+9z8A3njjjZzVngH69u1LeHg4e/fu5ZlnnmHJkiV89NFH7NmzhyZNmhTI/QghhBBCgARWIfKNi0nDYr/9eW37DqfNy+9jsYOrKffCTDfbDzO70pmUlATkDpaOBriTLTQf7zuCVr2GZDeGUfuzw7IX5/0rJCSEzp07U6FCBd57772cIbynTp2if//+7Ny5Ew8PD6ZOncrZs2cZPXo0e/bs4dy5rAq6h4cHgYGBnDp1CqUU5cqVk5WBhRBCCHFfkMAqRD7x99JJMd/+PP2PAJqYofD3zv0WVUrdUOlct24dLVu2ZPr06UDuYFnGLev5NnveqqPZbadb7LgYoYSLDPu93yUmJhIWFsaqVav44IMPqFKlCnXq1AGgT58+FCtWjPr166PrOhUqVGDJkiU4OTnx3HPPMXDgwJzrfPzxxwwaNEh+MSGEEEKI+4oEViHySR0vA0YNLLbbh0erPWsf1freRiArqNrtdjQtd8V10aJF9O/fn5EjRzJixIgbruPuoOHvbSAu/c76Gp+h0bKcAZPhxvBit99YJj5z5gxTpkzh+PHjOf29FbvdftNr/Ft3+3qFza5duzh//jxVq1YlMzMz57XPHiq+bt06unbtyqxZs+jYsSMzZszg8uXLhISE8Mgjj+ScbzQaC+wehBBCCCFuRQKrEPnExaTRvpKRKylZgfRW7EoRmaxoU97A5fBjQFbVVNd1oqKi+OCDD5g7dy7Xr1+ncePGuLm5ERMTA4DFYrnheo+VNZBpV3kKypA1b1bXIMA7d4DJru7+dRud7FBkt9s5ffo0Bw4cyOnvrei6jq7rJCQk/Kd5sVFRUURERORcLzY2Nud1eBDdKpRXr16dlJQUIiMjcXR0zDluMBiw2+24uroSFhZGeHg4YWFhVK1alejoaEqWLMnMmTOloiqEEEKI+5oEViHyUStfA+0qGIhMgbh0hf1vCyolZCguxGXS3NeIKSKUjRs35nx97dq19OjRg3LlynHixAkGDhyIpmkMHDiQDRs2AGAymW5os7yHTsdKJiJTbl/dTbcqYtIUL9Qw4umkYbPZcoJSdnX39OnTDBw4kLlz5xIVFQWAo6Mjbm5unDhxgsjIyJz7gRuDVkREBP369aN58+aMHz8+p+95ZbfbsVj+n737jo6i6v84/p4t2fROSEhCkwCB0EPvRISIUkKTJlIsgFJEFClKkyJNQEBAqoVeIl0poXcIgZDQCRCSEBLSy5a5vz/yyypK0edBwMf7OodzPJvZmTszm7if+d5i4sKFC6xevZp9+/bx2muv0aBBA0aPHs2CBQv+0v5edIXX77cPCn5Lp9PRuHFj1q5dCzw4S3BERAQhISFoNBrat29PuXLl2LZtGzVq1Pj7Gy5JkiRJkvQUyMAqSc+Qoii0Kq3j/Wp6SjpruJMFCVmCO1mC+Czw0OTxfZeSNHRJocUrzenSpQuHDx8G4N69e/Tr14+QkBBOnTpFRkYGNjY2NG7cmPT0dI4cOQI8vBIXUlxLE5dkYi9dISHrj9XWPLPgTqYgLQ/eCtJT1UuLxWJBo9GgKAq5uQV9ir/99lt69epFfn4+U6dOpXv37rz//vt07NiRlJQUNm3axKlTp6znCr8GraioKE6fPs369eupW7cuhw8fJjk5malTpz7xumVlZVmruRqNBr1ez5EjR5g5cyadOnXCzc2N8+fPU7x4ccaMGcOtW7f+k9vzQvl9UD106BATJkywfh4KeXl50axZM5YtW0ZcXJx1wqUff/yRffv2UbNmTb766iuioqLo16/fE9fulSRJkiRJepHIQUuS9IwpikI5dy3l3LWk5ArS8gWqKnDWqxR1cuVI85eZMGECX3/9NevXr2fRokVERkZy7tw5Nm/eTOnSpRkyZAitW7cGwN7envLly7NgwQLq1q370Eqcqqq8/3pNQkJfo9I7nxOdVwSLECiAAGy10KKUlhpFtXjaF7xfo9GwZcsWJk6ciMFg4MMPPyQmJoaNGzcyefJkbty4QfHixTl58iTHjx8HCpZL+fnnn6lcuTIlS5YECmaqXbduHcePH2fYsGH069eP2NhYWrdujbu7O4mJiaxcuZIuXbr8od3Xrl0jPj6eY8eO8eabb+Ll5cXOnTs5ffo0O3bsoHTp0gC4u7vTokULbGxs+P777/H393/6N+4ZEEJw4sQJihcvjre3N1DQ7Xrs2LEcOHCA7t27M3HiRLp160anTp3QarXo9XratGnDkSNH6NevH2XKlOH48eM4Ojry8ccfAwWzAEuSJEmSJP0jFY5Le5H/1ahRQ0jS/yKz2Wz9b4vFIoQQ4sqVK8LZ2Vnk5eUJo9Eo6tatK44cOSJWr14tunXrJpKTk4UQQmRkZIgpU6aIjIwMcfHiRREZGfnAvm/evClSUlKEEELExsaKihUrisGDB4tdu3aJHJMqbmdaxPU0i4jPtIg8k0WYzWahqqr1/X369BFubm7C2dlZODk5iZYtWwpvb29hMBhEyZIlRXBwsDh79qx45ZVXRExMjBBCiB9++EHUrFlTTJkyRezdu1ccPXpUtGnTRoSEhIjMzEzrvocOHSoWLFgghBBi2LBhomnTpg+0PTs7WwwePFjUr19fHD16VAghxNdffy0aNWokvLy8RKlSpcStW7fEuXPnRKVKlUT58uXFmTNnrO+Pior6727Mc/Tdd9+JXr16iV27don58+eLnJwcMWzYMJGUlCS2bt0qfHx8xKxZs0ReXt4D7zObzeLGjRti7ty54ueff35OrZckSZIkSfrPACfFQ7Kg7BIsSc9R4Rqq8+bNY9euXdy/f5+XXnqJmjVrMm7cOPR6PWFhYcycOZP27dvj5eVFz549GTZsGA0bNuTcuXNYLJYHljIBiIyMpEePHpjNZutrXbp0wWw2c/z4cfKz0vF11FDSRYOPg4KNVkGr1RIVFcWUKVO4cOEC27Ztw9fXl6CgINq3b8/nn39OlSpVMBqNtGvXjpycHDIzM8nPz+fAgQMAhIWFER0dzfjx4xk1ahQVK1akdevWODk5ERcXB0BcXBznz5+nRo0aZGRkYLFYiI6OJioqCiioBmdlZREbG8vmzZvx9PRkwYIFzJw5kwkTJjBq1ChSU1P56quvCAoKolatWuTl5XH16lXu3r3LmDFj6Nu3L9euXXtWt/E/JoSwdnUulJOTw6pVq5gwYQINGzbk5s2bnD17ljp16rB+/Xp27tzJwIEDrZ+dQoqiUKJECfr370/z5s2f5WlIkiRJkiT9bRTxX8zS+awEBweLkydPPu9mSNJTt3r1aiZNmkT9+vWxt7fnxo0brF27luPHj/Pqq69y79490tLSaN68Od988w01atRg+/btREdH06ZNGwICAh7Yn6qq1i7BxYsXZ/v27VSsWJE5c+ZgMpkICwtj8uTJdO3alZIlS+Lr64vJZCI+Pp7ly5fzww8/YGtry8iRI1EUhd69e2M0GnF1dbWOk23WrBk6nQ5VVa1rfMbFxfHWW2+RlpbGjz/+iJOTE19++SWNGjUiLi6OUaNG0aFDB1577TW0Wi2fffYZMTExHD16lM8//5xWrVpRtGhRa9uzs7Px8fEhLy+P/v3707FjR9auXUtSUhLnzp0jPz+fzMxMrl69yokTJxg5ciR37twhPz+fZs2aMW7cOGt34ReVEMI6zjcrK4tTp07RoEEDjh8/zuzZsylWrBjTp08nMTGR4cOHU6tWLfr37w/AiRMnSEhIsHYLlyRJkiRJ+qdTFOWUECL4969rx4wZ8xya89csXLhwzDvvvPO8myFJ/5HfBhMoWHrmp59+Ii8vDx8fH7p160bz5s3ZuHEj4eHhNG/enFq1arFx40ays7Np0qQJMTEx7Nmzh7CwMAICAqhXrx7u7u4oioLJZKJatWqULFmS0qVLo9FoyM7O5ubNmzg6OlKhQgWSkpLw9fWldOnSTJkyhZkzZ+Lu7o6qqmzfvp1+/frh7OxMXFwc9+7dY+vWrXzwwQfs3bsXnU7HggUL2L9/P2lpaXzxxRfs2rWLvLw8Nm7ciKurK6tXr+bUqVNkZ2ezfft20tLSSEhIIDAwEB8fHy5dusSNGzcoXbo0bm5uNGzYkKpVqzJo0CAaNGiAs7MziqKQnp6O2Wymc+fOmEwmXF1d2bp1q3Vm5PDwcDZt2kSRIkXYt28fOTk59OnTBz8/P1xcXJg7dy69e/fGzc3tOd7xRytcSxcKKqJZWVksX76c3r17c+bMGX744QdGjBhBjRo1mDBhAq+99hq+vr5YLBY2btzI8ePHWbNmDV9//TUVK1YkOPgPf9MlSZIkSZL+kcaOHZswZsyYhb9/XXYJlqS/SWHvhcKAUrhGampqKgsXLsTBwYGKFSty9uxZWrVqRdu2benbty9TpkwBYOTIkXz00UcAjB8/niVLllj3XbjuJhQsZdO3b1/WrVvHvHnzrMdOSEigaNGiWCwW1q9fzyeffELdunVxd3enePHirFmzhmXLlrFs2TJu3bpFXFwcOp0Oo9GIRqNhzpw5FClSBFVVGTduHPfv32fbtm0EBgbSt29fVFWlTZs23Lx5k5iYGJYuXYqqqty7d486depw69Yt62y9ISEhREVFceHCBVasWMHNmzcpX748RYoUQaPREBERQaVKlahcuTJvv/02sbGxrF27FkVRmDx5MnFxcdjZ2aEoCidOnLBWYzdt2gRAixYtGD9+vHWipxfNw5amuXv3LiNGjOD7779nz5497Nq1i6SkJNauXUupUqWoXbs2ixYtAiA4OJh58+bx0ksvUaFCBU6fPs277777XM5FkiRJkiTpWZKBVZKessIxiYVB1WKxMG/ePGbOnEl6ejpFixZFVVUOHToEQGxsLG3btiUsLIyQkBA2btxIVFQUr732Gj/++CMADg4O1jVW79+/T/PmzQkLCyMyMhKAAQMG0KNHD77++mt27dqF0WgkMzOTbdu2oSgKPXv2pFq1atSqVQuj0cilS5e4dOkSUVFRjBs3DicnJw4ePEj58uWJiorC1dWVHTt2UKJECe7evcv169cZOnQoOp2OY8eOUaZMGRwdHfHz87OeU82aNalfvz7ffPMNTZs2xcXFhSlTpvDyyy+TlZXF6NGjuXr1KoMGDSI8PBwoCHBdu3YlLCyM4sWLc/jwYeLj4/H29mbkyJH4+vry5ZdfEhoaSlhYGL6+vgwePJivvvqK6dOnEx0dbb3uv61ivwgSExOJjY0Ffg2qx44dY/Dgwezfv58iRYrQsGFDtFqtdT3bQYMGWR9MfPDBB6xcuZLmzZszZcoUvLy8GDBgAEOHDsVgMDyfk5IkSZIkSXrGZGCVpKegsMKYkpJinQxny5Yt/PLLLyiKQuPGjbl06RJjx44F4PXXX+fu3btAwcRLt27dYvbs2ezcuZMuXbpY18rs2LEjUBDGCqt0dnZ2aDQaKleuzKxZszh69CharZYmTZrQqVMnRo4cycSJE/Hw8CA/P5/k5GRrVdPW1paQkBDatGmDoihER0eTkpJC/fr1EULQqlUrzpw5g06nIy0tjQsXLtCsWTOGDBnC1q1bsVgshIeHs2jRIvr168eIESM4evQoAI6OjrRp04ZTp05x69YtPvvsMypXrkzr1q2pUqUKNWvWJDk5mXfffZczZ87QvXt3YmJiyMvLw83Nje7du6PX6xk5ciQmk8laeSyspubl5TFv3jzmzZvH4cOH6dat2zO9x3/FpUuXKFasGCNGjLC+Nm3aNAYNGkTRokWZPn06H3/8MU2bNqVy5cqcP38egD59+nDv3j02bdpElSpVmDt3LkOGDGHevHnodHIVMkmSJEmS/n3kpEuS9F84d+4cn376Kbdv3+bVV18lJCQEk8nEpEmTcHR0pGrVqpw9e5YtW7Zw+/ZtunTpwoABA9i/fz+NGjXijTfeIC4ujvXr17N3715GjBhB3bp1rfs/fPgwBw4coEePHnh7e1srdT179qRy5cq4uF7FG6YAACAASURBVLiwcuVKPvroI8aPH09MTAwuLi4kJSVhY2ND2bJliY+PtwbpmjVrWgNpZmYmLi4uXLhwgaioKFq0aIGiKGg0GmrWrMn9+/cJCAigYsWK7NixA7PZzNKlS9myZQuLFy9m2rRpdOrUiarVqtOm/2jSS7ckJS2D08tGE1CmDDE7V4DFhMFgYN68eTRo0IBXXnmFmJgYnJycMBqNtG3bllq1atGrVy+qVq2Kp6cnaWlpWCwWWrduTWZmJkuXLsXb25uIiAgcHR2f161+KCEEFkBLQUXVx8cHgLS0NDp06EB+fj6ffvopr776KgMHDiQ0NJTQ0FBu3rxJkyZNOHr0KBs3biQuLo5evXoREBDAhAkTMJlM1ocbkiRJkiRJ/waPmnRJBlZJ+i8UVsx+W0mLiYmxjuOcOnUqo0aNYvv27bRo0YK9e/dy5MgRvvjiC9544w0WL15sfV++RRCbopKUoxJ5ZB/r5k3CQa/g7uaCwWCgY8eOtGvXDqPRyLAvviLXrijFGnZm/bi+XNy/AWdXD8oHBnL14gVeKluew/v2oNVqMZvN+Pv74+XlRbNmzZg6dSrFixenXbt2HD58GC8vLzIyMjh9+jQGg4GJEyfStWtXRo8ezaVLl1i0aBFarZbMzEx69epFUFAQzs7OpKZlENB7Jou+XcTtnxdT4rNDKApknvuF5NWfYEq8jI2jK2UDAvhq6iROnjzJqFGjOH/+PAcOHKBfv374+PiwefNmTp8+zalTp2jTpg27du0iKyuLiIgIgoODGThwIDVq1Hget/ehLEIQr+ZxxpLBDTUPASzp9A5xR06xZMMa2jZoSkx0NHPmzKFcuXJERUWxbNkyXn75Zb744gtq1KiBTqcjLCyMevXq8cYbbzBixAiaN29Ojx49nvfpSZIkSZIkPRePCqyyj5kk/QkWVXDpvsqROxbu5Qh0WrBJPMvBw0c5sG9vwTYWC1qtlsDAQE6cOEH9+vUJCwvj008/Zdq0abRo0YKmTZtSrlw5Vq5cSdmyZVFVFZNQ2B1nJuKWBaNFoFHgxs1sLl65Sp8fY6nuqXJ9y9dERkaiBobyfayF/Qcuo/EwoUQvJOXgVtS8PPKc/DibkItXw76YHA289nI/Di8YTkbSTbp178G0qV+SnJxMqVKlSEhIIN/Wkys3bnH69Bk8PT356aefGDFiBHv27GHWrFk0bdoUnU7Hjh076NOnD1u2bKF8+fJ888037Dl8kldDW+KuqY9r3d64Jd/BUZOPRm9A1UNS+l20ju54DVhNzJwOfDl/GdvXLGfGjBmMHDmSgwcPoqoqKSkprFixgnHjxrFr1y4mT57M/fv3WbVqFaNGjbJWLF8U91UTm03JpAoTBhQ80KNRFGo0b8r5Lb8wa+UKzpgy+KhJKyIjI2nbti0nT54kNjaWpk2bsnTpUipVqoROp6NOnTo4Ozvj5+dHt27dqF279vM+PUmSJEmSpBeOHMMqSU+QmK0y6ZiRBZEmLt1XybUI0vMFZ3PcSczTse5cGhZVoNVqreNMY2JiqF27NsOHD6d79+7s3r2bffv2AVCsWDFOnDjBJ598Qr6qsPCskR3XLbgawM9JQzFHDXWbtgCLCbvcRE4nazhw28SxDBemnDBz7b7AvnRN7q0fjeXqIUq8twS0esx6J7LjznF9w5ckJSej1+up3XkQFgHhe45RrJgvbu4eeAXWJi8vnx+XL6Zko44Evf426blGrtiUpe+7/Th58iSvv/46c+fOpUuXLmzbto2UlBT8/Pw4evQo+w8f44OpK9AHNMLOxQ0HG/DrMAqNvmAiIOdKL4NGg2rKxxS1BcXelb27dlG/2SvodDo2btxIvXr1+PTTTzEajYSHh3P9+nWuXLmCm5sbO3bsICAg4IUMq2uMiWQLM16KDS5KQVgFaP5OT1yLeVO0iBfHt/3M6PAf6dyzB2lpaQQHB7No0SKGDBlCSkoKQ4cO5a233mLFihU0bNgQKJjl2NXV9XmeniRJkiRJ0gtJBlZJeox7OSpzThvJNgn8nBU87RQc9ApONgrFnG0oXq4SP27ezabLZsxms/V99+/fJzs7m5UrV7J06VJatGjxwH5tbW0BWB1r5mqawN8JbLS/znKrKAqh/cexZ/FEtPcucuSn7zhy7CTGpGsIBRx8SlOkSS9KvrcYtVgV7Mo2IP/CL2hdfHBu8CaegXXYMvFtXLz9Mdg5cufWDYS9O5dv3ObMvu04uhdBr9NQ8qUATKm38PQtxbuNirN4837mLVhkXVqnS5cu5OXlsX//fho3bkznzp15b+CH3NV7U7rfEtyDmvzhmimKgnfrYejsnMmMPYBiMWM25nLi2FF0Oh06nY4OHTqg0+nQarXodDoyMjI4cuQI69atw8vL64Wb8VcVgm2me1gQuCj6h27TqHsnbkSeo2Xv7mwaN5Ula1cTFBRESEiIdR3atWvX0qBBA2rVqsWpU6cIDAx8xmciSZIkSZL0zyIDqyQ9xparZvLN4GH3xwDl6O6Fh29J7kfvY/eVTOJzNGg0GrKzs7G1taVSpUrMnj2batWqsX37dho3bvzA+5OyVSLvWvB1fPiSLNVavMHx8KVsmDIQt9ChKIqGSwv7kXV8HcZ7ceTdvU6uUUXj4I59hSag1ePaoAeqKtCXqoWtpz8nd4VjsHMg6+5N4q/FYrGYKeJfGv8KwQTUCsGYm0W5Os0Z8G0EBjtHag6Yi/pSwwfa8eqrr7Jw4UI0Gg3DPx1B4KideLQYiI3No0cUFA15G1NaInkJlxCqGa+Wg3Cu3Qmh0VGmTBk++OADpk2bxkcffURsbCz16tV7YI3SF02iyCdZGHF5zCiKtsMHcn7vQRxcXXhz8ucYUbmVlUb58uUpW7YsGzduBKBbt270799fLk0jSZIkSZL0J8hJlyTpEe7nCcYezqeYI9aun7+XnnyHHfM/JyMzm54fDKdk/mXmzp1L9erVmTJlCvb29o/c/09XTOy7ZcHH8dHVxP0/ziY+OY2cpp8iLGbSz+8mc/uXFG35PoreFgJD0ShgSUsg/uvOWHIyMPhXQs1IxHL3Mg7+Qehzk8lKSaLipOOUdNFRo5Q75yN+wt23FH7lq1nDcsR3M8jKSKdu7zGMrW9g3eqV5Ofn8+abb3LkyBEaNGjAxVQL7cNz0QCGJ4yAj53wCqb0JCpNPYtqsRC3eRb2l3eycv6XWCwWGjVq9MR78KLYbkrmmiUXt0dUVwstem8oQgjeWTCDa7dvUdWrGK85+ZGamoq7u/szau0/S+HYb0mSJEmS/t3kLMGS9BedSbKwPNqEn9Pju6fmZqaxf9Vcbl+9SGnbLD788ENCQkKeuP+JR/MxqwJ7/aP3b8rP4/PQUrz0VRwWVcEswCHrJrZFimNSIcsIhT2JTemJ3Pi0ErqiAZgSYrGxd0Ln6I5nUR/8a76CTcgQbLDQrGRB6Pp9RdOUn8f4VqWo3XM0GcfW4e9ThFGjRhEc/OvfjZ+vGxmw24iLDTyp1258+BQSf5pG0VcHkX5mG1p3f2p3/YRdw/45QbXQwrxbGNCgVx5fBU5PSmZAyap8E38eGzcXLAj62Po9o1b+s8XFxVGiRInn3QxJkiRJkp4TOUuwJP1FRsuf287OyZVX+o7g2t0s5rbyeGQ19vfyLQKbJ/SC1Rts8W74Bjlnt2Oo/CqKAINn8YIf/u5Zk97FG9eX+5N/6zxF3lpIheKeqI5F0J5YjmJwwGixYNFpH9n1Vm+wpU67PsSd3M30qV/TtlG1P2yTYSw47p85Rb2jB45lamHOSqFU3/moxapg7/ZijU39s4wI7Hhy212KFqHr5M8w5eVjEGBUXvwHgs9SVlYW69evp0qVKlStWhWAvXv3MmPGDLKzs2nVqhWNGzcmODgYVVVf6G7ikiRJkiQ9GzKwStIj2On5ExGlgEkFV2fHPx1WARz0CjkmwZNGMlbpPYWrGQpmy4MZ9WGH8mw3BhQFkwoOngrZJqj+em/MaDmVpGLzhJ6Xof3HcycLqlW1eejP3W0LDipUeEKxkSLN+lCkaW+U/w8dmUaw1/0zA6sBDRYE2j/xiXh10LsA5AsVwws2edTzIoRAURQMBgObN2/GYrFQrlw5bG1t2bx5M+PHj8fDw4PevXtz8eJFgoODZViVJEmSJAmQky5J0iOVcdWgVcCkPrlKdi8X6hX7a+PwgotqSc9/8nal3HQIAVoN6DS/BlWtUhCoHwyxCmYVHPSgioLtXe11eNgVhFhv+8cHqBwzuBjA7xHjal9y1eCgh3z1ye1WFMUaVoUKqgq1/+I1elEEaO3JxPzkDX8jEwtlNQ5/U4v+GiEEFsuf7DLwlI8Lv04qptfrCQwM5MKFCyQmJnLlyhUOHTrE7t27adeuHaVLl2bUqFHPvJ2SJEmSJL24ZGCVpEew1yvU9dVyN/vx2xktAiGgls9fC2PVvQu2Nz8hEPs7KdjpCoKpXgOFw84VpWDiI/U34VFQ8HMvO4VcM/g7K+g0CooCRewVdFqFR41bV4XgXq6geQkdWs3DA2txZw1VvTR/urt0oTwVHGygQ9l/ZqeOIK0jFsEjr93vCSFQEVTUOv7NLXsyVVVRFAWtVovJZAL+/Hn8p4QQ1uMWWrFiBTVq1ODmzZusWLGCgwcP4uPjgxCCw4cPc+DAARYsWEDx4sWJjo7+w/4kSZIkSfp3koFVkh4jtJQOH0eFhCyB+pAvzblmQUI2hAXoKOrw136dXA0KISW0xGc+fN9QEDagoLKpVcDZpqC6Wbi5rRY0GgrCFGCygL0eDFqBTgP+ThosquB2pqBHBR1Vimi5lVnQ7t/KNgluZgjqFtNR1/fRwVtRFPpW0qFVIP9PFhxVtWA8cG1vDX5O/8w/OR6KnlJaO1Iw/antUzBRRmOHm+bxswr/XdTfPMXQaDQkJiYybNgw6tSpw927d//2dW4VRUGj0ZCcnMy0adMA+Omnn/jiiy9Yvnw5PXv25MSJE9y8eZNevXqRl5eHnZ0d0dHR9OjRgx07dmA0GlFV1dqdWJIkSZKkf6d/5rdHSXpG7PUK/avaUNFTw50siM8UJOcIkrIFtzJVck3Qo4KOBn7/WVfX0FI6GvrruJUBafnCWkkqDBy5FoWbGYImxXUMr6VD+f9uwHlmsKgFVVZHfUGAzTODjRaK2gOKQjUvDel5cCcLmpfU0TZAT9/KetqU0ZFnhvgsQUKWID5ToADdAvW8UV73xHG4dXz1tA/QkW95cmi1qJBpAj9HGF3P8I8NHoqi8IreAw9FT7IwPvoBgxAkCyOeig3N9Z7PuJW/ViJ/O/4zJyeHXr164ejoSHh4OF5eXk/9uL/vbpyVlcV3333HO++8Q2ZmJlevXsVgMFiXrxk4cCA3b97k8uXL9OvXD3d3dzp37sybb75J2bJl+eCDD7CxsUGj0aAoChcvXuTHH38kMTHxqbddkiRJkqQX2z+zf54kPUOONgq9K9lwL0flbLLKvVyBXgNl3DSUd9dgo/3PQ5hWo9CxrI5ybgpbolM4dyURzxLlQCgIBFmXj5ITuQWvl+vRPKQZVYo6seJcPntvqeSYf62sOujB6//Hp9rrwddRg9GiUMtHS91iWvycFBRFwUYLISV0NPLTEp8lMFrATge+TsqfnjBKp1EYUc+ACqy/bCY/HwzagrBcuAuLCrn/H6pLuMD8l23xdfpnjl8tZKdoaW9TlD3mVC5ZctAIsEeLFgULgmwKQls5rQNNde4YnjQr1d+g8IHA4cOHWbx4MV26dKFYsWIUKVKE+vXrc/v2ba5evUpgYOBTDa6FQTQtLQ1XV1fu3LnD2rVrEUIwduxY8vLycHZ2JjMzk/z8fPz9/cnMzGT58uXUrl2b7777jvj4eLy9vR9Yk/X27dssWLCAX375BU9PT1asWMGGDRseu76xJEmSJEn/W2RglaQ/ydNeQ0iJpx9CFEWhipeOQ+vW07lCdUoG6VEFRGxezaK5k+natSvr167h0IH9TJw4kWnN7EnJVbl838LNDIEAijkoONhoKOOqYNAVTLxk0BaEy4fRaxVKuvznQdtOpzC2gYEGvhqWnjcSk1qw5M1v9+jjAK3L6OlRQUcRh392WC1kq2h5VV+E+joTMeZsrqm5mFAxoKWSxpFAnSPOyrP7s2qxWB4IeCaTiRkzZrBhwwZ69+5N3bp1cXBw4KWXXmLOnDlUrlyZjRs3EhwczLhx4yhevPh/ddzCpWcOHjzIqFGjcHZ2ply5ckydOpX27duzefNmEhMT8fb2pm7duoSHhxMfH09gYCBOTk5UqVIFKOhR4Ovr+8AxkpOTmTVrFtu3b+f8+fMAhIaGEh4eTocOHdDrn093a0mSJEmSni3lnzCZRXBwsDh58uTzboYkPTWqqnL9+nXc3d1xc3Ozvn716lXS0tKoUaMG7dq1Y+jQoTRo0ICLFy8ya9YsqlatyjvvvPNCjetTheBSqoUjdyzczyuo2JZzU6jtq8dB/2K08X/Jw9YnvXLlCmXKlMFsNtOsWTOWLFmCl5eXtavu7z9jw4cPZ+nSpTg6/rVJoR527KysLN544w26du1Khw4dqFevHgMGDKBixYqsWbOGoKAg3nrrLXJzc4mMjGTOnDlkZmbStWtXihUrRuPGja37+v7779m2bRutWrXijTfeYO/evYwdO5avvvqKGjVqsHjxYg4dOsTYsWPx9/f/q5dOkiRJkqQXmKIop4QQwb9/XY5hlaRnSAhBUlISr7/+OuPHj+f+/fsALFmyhFWrVvH5558THh4OgJeXF7t37wbA39+fihUrcvPmTYAXJqwCaBSF8h46elUy8GFNA/2qGWhW0kaG1acsJSWFSZMm8duHd/Pnz6d69eoMHTqU7777Dp1OR6NGjejYsSMDBw6kT58+BAcHo6oq0dHRjB07lk6dOlGqVCl0ur9eCS4Mq9988w316tXjypUrXL9+HUdHRxo2bIiNjQ2TJ09m69at+Pv7U65cOY4dO0ZGRgZ2dnbUrVuXRYsWsXnzZrKzsxk0aJB132PHjuWHH36gW7duLF68mDFjxlCiRAlCQkKsvwddunTh7t27HDx48Lks0yNJkiRJ0rMnA6sk/c2MRiNCCJYuXYrZbMbW1pZbt24RHx/Pu+++S6tWrThz5gyhoaG888473L59m0OHDtGxY0cOHTpETk4O9vb2XL16ldKlSwNymY9/k/Xr17Njxw7c3NyIiooiKioKi8XCtm3buHHjBvv376dnz57Mnj2bTZs2MWHCBHbt2sWcOXPYsGED3t7enDx5kqSkJPLz81m1ahVffvkltra2jz1u4dI0v/f++++zZ88eJk+eTJkyZRBCYDKZMJlMCCF4+eWXOXPmDElJSdSoUQMoqACrqoqqqjg4FKxN27lzZ1xcXNi6dStQMF71vffeo1WrVsybN48zZ86Qm5tLUFAQ165d49KlS9jb29O0adM/LJkjSZIkSdL/LjmGVZL+Runp6Xz66afs2bOH+Ph48vPz0Wq1xMTEcP36dZo0acLWrVvZsGEDLi4uNGrUiNWrV3PmzBm6deuGu7s7b7/9Nnq9nosXLxIWFga8WBVW6e8xb948li5dioeHB/Xq1aNWrVqEhYWxe/duGjduTGhoKPXr12fMmDHs2rWLsmXLEhERQZUqVShVqhS7du1i+/bt2NjY4OPjg7+/P82aNftTxy7scq4oCikpKWRnZ1O8eHGuXLlCZGQkBw8etG5XuXJlPDw8+Pbbb+nduzcGg4Hg4GD8/PxwcnJi5syZGAy/zhCdm5uLnZ0dTk5OtGvXjm+//ZZWrVqRlZWFm5sbubm5lC9fHq1Wy4EDB+jcuTM7duywnuPQoUP/tmsuSZIkSdKLRwZWSXrK0tLSSEhIYN26dWzcuBF7e3uSk5MxmUyMGjUKJycnnJ2dsbOzIz09Ha1Wy4IFC2jbti0Wi4XQ0FA2bdpE06ZNWb58OUePHuXs2bMsWLAAg8HwvE9Pegaio6PZuXMnixYtomrVqtbXW7RowdatWzl27BilSpXixIkT3Lt3j8jISCIjI+nTpw/16tXD1dWVzz77jLZt27J9+/YnVlOBB8ZFK4rC7du3mT9/PuvWrcPX15cZM2ZQoUIFjEYjkZGRVK1aFZPJhI2NDUOGDCE8PJx3332XlJQUevXqhYeHxwMPVmJiYpgyZQp3796lefPmDBkyhLfeeotly5Zx+fJlatSowU8//YSPjw8BAQFUqlQJZ2dn3N3dGTJkCOXLl3/6F1qSJEmSpBee7BIsSU9Ramoqffv2pXbt2nz11Ve8+eab2Nvbo9frsbGxIScnh6VLlzJo0CBSUlKoUKEC06ZN4+eff7bOvBoaGkpmZiZnz55Fq9XSuHFjBg4cKMPqP5gQArMqntiVu/Dnx44dw87OzhpWC8drOjs7U7duXY4fP05KSgqZmZns3bsXKOh26+LiwtWrV3F2dubw4cN8/PHHTwyrhfv+bbg8c+YM7dq1Q6PRcPHiRerUqcOPP/7IxYsX6dSpE3PmzAHAxsaG8PBwvL29GT58OLNnzyYyMpJBgwZZ95eWlsbt27cZMmQIzZo1Y/Xq1XzzzTcsWbIEV1dXXn/9daZOncoHH3wAwLBhwwgNDeXnn3+mSZMmaDQaKlas+MBsyJIkSZIk/XvIWYIl6T9UWJFSVZV58+bx7bff0rhxY3Q6Hd999x3379/HxcWFVq1a4ezszNatW7l//z6VK1fmlVdeITw8nLt377Jw4UJatGhBnz59+Pbbb4GC8Xx+fn7P+Qyl/0a+RXDhnoWIWxZuZwosAjQKuBkUGvtrqVZUy+2rsRw9epTXX38dT09P63sHDRpE5cqV6dOnzwOfM41GQ2JiIh9//DFt2rShbdu2tG/fnlu3buHm5saECROoU6fOE9sWExPzh+Nu374dRVGoU6cOWq2WNm3aUK1aNaZPn86FCxf4+uuvadK0KfUaN6J5k6Y0b96cM6dPo6oqixcvply5cn/oqr5o0SJSU1MZPHgwRqOR48ePM2nSJGsvhPj4eOLj4wkNDWXNmjWUL1+erVu3otVqadmy5dO9IZIkSZIkvdDkLMGS9BRkGgV3c1SSsy2YVJgxYwZ2dnYMGjSIe/fucerUKdzd3cnLy0NVVRwdHTl69Kh10hxFUYiJiUFRFBwcHEhKSmLcuHG0b98eDw8P63FkWP3nsqiCX26Y+PxQPsvOm0jJExR1AD8nBW8HUBEs3BlJlcatCG33BhdiL3H27Fmys7OtFdZy5cqxePFi6z4LJz/KyMigaNGi1KlTh+3bt5OSksLy5cv5/vvv2bVr1wNhNUuYuWDO5JgpjWOmNKLNmRyLOsNrr71Gly5duHz5MpGRkVy5coWuXbsyfvx41qxZQ1hYGFlZWQwcOJBr166Rl5eHX2BZNP5FWXxsLyvMifTav478ZtWo9X4vDK7OpGdlWdsYGxtLVlYWUPBQJyIiAoPBQE5ODjNnzmT48OGcPHkSs9nMypUr8fX1pW7dutbJl1q1aiXDqiRJkiRJVnIMqyQ9gckiuJBiYWWMmSN3zOSZ4eKXbfAqV4PEPUvxKupNg/r1uHHjBhUrVqR8+fL07t2bVatWYTQaiYuLo379+tjb23P37l0yMzPZsGEDL730En5+frRr144PP/xQTqT0PyDfIvjhgonIuxaKOih42j34TFCjKDjoIW7XMgKq1qFip49xs1OoFKTFwUGHyWRCr9fTv39/JkyYwO7duwkJCbF+Ng4ePIjZbKZt27YYjUZsbW1xdnbGxcXFeoxk1cgpSwYXLdkIBBpR8F5VEWxcMJuStauw5KcNeGlsAIiMjCQ1NZXDhw8D0LdvXzZt2kT9+vVx9/Bg9A+L8O3WGpv6Vcha+AOW63coGVwV3xavEH3qNEcOHuL9KePo3rotbRuFMHz4cBo2bMjQoUMJCQnhl19+ITU1lejoaDw9PalduzaJiYn4+/szYcIEunTpwuzZs2WXd0mSJEmSHkpWWCXpMVLzBB/uyaXrljxWxZq5liZIygZDyBAub1tIZloqatWOeFVpxoEDB4iLi2P+/PmcPXuW/Px8zGYz1apVIzU1leTkZBwcHEhPT+fcuXO4u7uzZ88ehg4diqqqcqmafzhVCFbHmom8q+LvpGDQPvwBRPzFSOLOH6dx1w8o5qiQmm3m2/MWsk0CvV5Pbm4u9+/fZ+bMmcydO5ePPvqIa9euMWTIEIYPH45Go6FYsWIMHjwYZ2fnB/Z93ZLDSmMCVy05uKPHSzHgqbHBU2NDztlL3D5+lqoD3mSVMYGL+RkA3Lp1ixIlShAfHw/A66+/zoEDB/AvVRK72kFs+m4lnugJrl2bt6aP56XggnG1iqLgZGOLd+kSvDLwbcaN/pwLmSlMmDCBbdu2sWrVKpKTk/H19cXV1RUfHx9cXFxo06YNYWFhjBw5km3btgHIsCpJkiRJ0iPJwCpJj5CRL/hoTw5br6tkm0CnAXu9gkEHrkFN0No6gRCkZ+Xw3db9VKnblAMHDhAREcHBgwfx8fEBIDk5mZiYGIoVK0ZERATz588nJiaG+fPn4+/vjxACrVYrK6z/cJFJFo4nmPF3evyyQw6unuh0eoy52QB4O+tIzlH56YqJZcuWERQUxJQpU+jcuTPjxo1Do9FYlzbas2cPr7322kP3G6/mEW5KxhEdbooe7e/a4OTpgd5Gj12OCSd0bLWkcNOSi5ubGwaDgZiYGABCQ0M5f/48e/Pv4dYomFd6dUc1mxFC4ODq8sA+S1SuiBACbx8f2o4YwrQlC4lNvM20adPYunUr+/btY8eOHZhMJgIDA5k0aRLvvvsuP/30E+3ataNEiRL/zSWXJEmSJOlfQAZWSXqEBWeN7LstEAIMWigsmIn/H6vn0ugtAPKitpObmkRO9R5079ufmJgYVFUlNzeXokWLkpiYaJ3t19vbm/fee++BL+oyqP7zCSH4Jc6Cu63yxPup1enxDqjElZMRAKgW6RA0lAAAIABJREFUC172cCJRJTk1ne3btzN58mQAgoKCmDx5Mr/88gtffvnlAxMz/ZbZYmGn8R4OaLFVHv5nXafX418pkOiIgxgUDQ6qws+mFOrWr4+zszOTJ0/m22+/pWPHjjRu0Zwb9lCuRGnqv9EOnV7/0PO6n5BExaYNsLGzpXztYK4dPE7/vm/j7e1Nz549iY6O5tKlS0RGRiKEwN7ens6dO+Po6EhCQsJfuMKSJEmSJP1bycAqSQ+RmifYfNWEWQX971bTUDQFvzZuoR+BTo/l/m1s/ILIMXhSocd4Zs+ejV6vJyAgABsbG95//33MZjMff/yxdR9yiY7/LbcyBXeyBU42T97W0d0L92IluXIyAmNeDhqtFr1WQ25mOlk2HpQsWRJVVa2TGGk0GjSax/+pTlRMZGDBTjw6LDt7eeJVsjgXIg6Rn5ODk95AFhYuZ6ZQpkwZ3nvvPaKiomjZsiUdJ45Ep9GheUL4di7iweWjp/iybQ9mduxFzddbULFtKB98OITc3FzKlCnDwoULqV27tnWmY4DBgwczf/78J18sSZIkSZL+9WRglaSHOHjbxJ2sgmVIHvaVXagqWq0W12bvgWohY/dcUtcM58MGHsydOxe9Xs/06dM5deoUM2bMsH5Rl/43nUq0oFf+XLVcURSqteiMxWRk9di3ib90ljM7VrF1VDt2RhzCZDI9MaT+fszz7rhLrH5nGN8NHU3yjVuPPG69zu0wG40seHsIN86eJ2rVT7Rr+SqnTp2iTZs2zJ49m77vvcs5SxauPPmhilano1T1yjh7ejDt3EHafjyQ1hM+odKrIRQtWtRaUS1c+mbw4MHcvXuXtLQ0OnXq9MT9S5IkSZIkycAqSQ9xMknFrBaMW32YwiqrV8cvUBzd0Tp5YLkfj2o20aXXu2RmZhIUFFSwraI8sUIm/bMl5Qhs/8Kc6y5FitF6yFS8X6pAxIrpnN65ihbvjKb+gJnY29s/8f0ajQZFUTh37hxnzpxh04rv8StbBht7O7bNXkDilWsPfZ9bMW+6Tx2LX4VybJk+j1OrN9Fy9BDmzJmDXq8HIAcLFgS6R3Qt/j13Xx8qNW8MgCk/HzudDdU7t6FWrVoMHTqUzz//HI1GQ506dUhNTWX8+PGcP38ei8UCIB/mSJIkSZL0WHJZG0l6iBzjk2fsFaoFRaPFPaQ/5tRbOLQYiqtPSUa3tkVVVRlS/0WMloJq/F9h5+RK874jyc/JwmDviBCC25kgeLCqf+/ePTw9PR/4TEVHR/PNN9+we/dufHx8SLHX8eHapaRev0nEspWc2baL0IHvPPS4Dq4uhI38kLysbPQO9mRgfuDn5r84WbXZaEJYCiq+eoOBfGHmwqkzzBo9lZ9//plVq1YRFBRE7969+f7771m1ahVz585l2rRpfPbZZwQEBPy1A0qSJEmS9K8iv1FL0kO4Ggoiw+O+uysaLUIILFkp2Jauid6zJDZasLN58phD6X+LvR4sfyHo/baqaLB3BAreb9BhHTcqhGDFihUUK1aMkydPWrsAnzhxghEjRpCRkcGFCxcYNGgQyVdvYEHgUdwXv4rlSLxynXs3bz+2DbaODlgQGH73vwEbRXns577QpSMnmNtzAMfWbyZyx24O/rie1PgEtk39mjWfTaZv375oNBqmTZvGxIkTWblyJUIIfHx8GDVqFGXLlmXgwIGMHDnygf0KIazV1+dJCCGXmpIkSZKkF4D8Vi1JD/FyCS22WrA8obeiMfEiCBW7Cs0QQCkXBR8HOevv8yCEeG7dS0u4KOSYHr/Nb8NP4QONU9t/tM4WnGkEPyeNdVtFUaxrpC5fvpy5c+cC8NJLL1GtWjXS09MBaN26NY62duxfF45Wp8M/KBCL2czprT8/sd2ZmCmjfbALsj1aHNGSJx4dGrPT0pne/i2cPN2ZfHI3zfr24MrxU9w4ex4LkHT1Bm+88QZCCDp37kxGRoZ1feLNmzej0WgYPXo0c+bMoXPnzg/sW1EU66RksbGxTzyHp6Xw/hR+jhTlwRmfZXiVJEmSpOdDBlZJeogqRXUEuCmogPqY76kGn/J4dZkKLn7oNdA9UI+NVgbW/9bjwsHDQmlhwNBoNM9lbGR1Lx0CUB/TbuU3ldMLB7ay7etR3Dh7BFdvfwCyTYKGPuKBCZWCgoJwdXXll19+YdKkScTExODu7k6zZs3w9vZm9+7dAAzoP4C9c74tqGAGlKZh947U6dDmsW0WQmABKmodH3hdqyhU1zmTyR8Da2G7HFxdaPnB2+Rn5wBQs20o8TGX0dvbUaVNSwLLBHDo0CFr+Ozbty+RkZEsX74cjUZDcHAwAKVLl6ZSpUoP3CtVVZk0aRLVq1dn5syZDBgwAKPR+Nhz+W/8NqDCr2POs7OzmTNnDuvWrbO+LkmSJEnSsycDqyQ9hJ1O4c2Keux1BeMTH1dcMVkKug6XdYPGxfXPrI3/ixITE9m3b99Dw0FhF83C6uRvQ45Go+HOnTt88skn9OjRg7y8PDQazTMLrR52CpWKaEjJ/WN7C0OeKS+Xc3vDCyZL2ruJKycjaN53BGkJN/l52TTs9QqBHlo0Gg1bt25lyJAhpKamEhgYSPny5enWrRszZsxg27Zt1K9fHz8/PyIiIgAY2PdtlDwjFy9fRGdjQ7l6tXAu4vHYNqdjxlcx4Kn88TNbTuuAApiESvTeg2ydWbAEjfjN9WzYrQPXz0RxPyGJ7PvpaPU68HKjYZkKvBwSwsqVK8nLy6N169bMnj2b7OxsFi9ejLOzM82bNwd+nTxKo9GQnJzMmTNnuH37Nvn5+URERDBgwAC+//57Vq9e/V/cnYf7bUVVURQSExMZMWIEa9asYe7cuTRp0oSkpCTWrVvHrFmzyMrKeuptkCRJkiTpyWRglaRHaFdWT69Keuz1kG8pCK6qKAinAjCrBa+rQElnmN7EFg87WYX5TxSGB2dnZ4YNG8a+ffsYOHAgly5dsm5T2EUzJiaGAQMGsG3bNhISEoCCrqMdOnTAzc2NcePGYWNj88wnvmpWXEeeWWC0CGsIKvyXcS+B0ztXcmLzcm6eP069Du/h7OVL6p0b6Ay2HN+0lEbeFnb/spPq1avTtWtXjh07xqxZszh79ize3t5ER0ezZcsWPvzwQ+7cuUNQUBDXrl3j+PHjAESeOkWJgADSxBP6JgPpwoQeDS1sPB/6cMBe0dJAcSEFE1pbA7u//R5Tfj4ardZ6rzyL+1GxaQM+qdaEuT0HUKV9K14KLE99Ow9cXV1Zt24dM2fO5OLFi0ydOpX169dTvXp1tmzZwpEjR6z7iYuLY8OGDfTs2ZPExERWr17N6dOnGTx4MO+88w4TJ06kR48eT+0+FVbgC4+v0WhYvHgxL7/8MoqiUKdOHQICAtiyZQsDBgwgLi6ONWvWcOTIkafWBkmSJEmS/jwZWCXpEXQahQ+Dbfi0tp4yrgVL3Bj/P6Tm/394dTFAYz+FJaF2BHrKSbf/qsIKqKIoqKqKvb09RqORDh064OLiwsWLF63BIicnh+nTp9O9e3cCAwPZtm0bLVu25MaNG1y9ehVXV1cGDhyIyWQiNjYWIQSHDh1iwoQJHD58+G8/l+KOgg5l9SRkgen/C5E7vhlD+IyPiPx5LRcObMO3bBXO7Q3Ht1xVHFzcSbwWQ7rWHc8iRZg1sAPz589nwIABJCcn07dvX2JjY4mKimLJkiU4OjqSkJBA+/btGTFiBLm5uQwfPpxatWoB4KIz0MFQFEdFx101nyxhfqBrtRCCbGHhrjBip2jpYFMUZ+XRn9lqNi7U17qi+rjjXtyXfctXFeznN1XWup3aYMzLp+M3U2jSqyvlLibzZvtOjBgxgoCAABISEsjPz0en03Hx4kX69etHWloaZ8+exWwumJ14/vz5fPDBB7z//vuEhoYSFhbGjh07aNmyJUePHrVej+vXrz+V+1QY0I8ePcqGDRtISkpi3rx5pKamYrFYsLe3p27duuzdu5cOHTrw/vvvU6NGDQ4ePEhGRsZTaYMkSZIkSX+e8k+YSCI4OFicPHnyeTdD+hfLyBccjTex8aqFtDyBjUahrLtCh7J6yrhp0P7VNU2kB0RERKDT6ahduzarVq1i7Nix7Nq1i/nz56MoChaLhQ4dOuDo6Iivry+dOnXiyJEjaLVaNmzYQKNGjWjatClFihTBz8+PTZs24eLiQpEiRejcuTOrV6/ms88+o3Hjxk99LGJGRga2trbY2NgghGDhlsPsum4k/cZ5Mm+e5+wva/l0Ywzfj3oTR1dPTMZ8Wr43hugTB/hl7nCEKR8XJydMJiMVKlRg//79TJw4kT179lC7dm3mz5+PEIKZM2fSu3dvMjMzSUhIIDAw8KHtMQmVa5YcTlkySBYmNPzaK8BD0ROsdaa01h6b362zarFYrJMdAVy/fp1evXrh5OPFrfRUMtLSGbpnPbZoUM1mVI2GPI3KmvdHYm9U2bD8O5bMnsuQIUMoUaIE169f5+DBgwwcOJBXXnmFnTt30qBBA1atKgi+3bt3Z9CgQXh6elKtWjVWrFhB3bp1AWjVqhXVqlVj0KBB7Nmzh/9j777Do6q2xo9/z/RJJr2QRiAhEEgoIUAIvfcu0kRKEAUElCaooBAsiBhAihQRkVCDgAGlI4ReQ+hJIIWEkEZ6n3Z+f8REEL3ivfL63vd3Ps+T54FDZmbvczJD1l77rPX111/zxhtvMHTo0L98fUwmE9nZ2djY2KDVaklOTmbixIkkJSXRuHFj+vTpQ0xMDJGRkZSXl+Pu7o6rqyulpaWMGTOG0aNHs3DhQiIiIli2bFn1dmaJRCKRSCR/L0EQroqi2Py3x6WUkETyHKzVAt29VXT3/qdH8t+ranHsyYDx5MmThIeHc+fOHfR6Pe+88w6jRo0iMjKSL7/8ku3bt6PVavn4449p0KAB1tbWhIaGEh0dzdGjRzl48CCrV6+mc+fOREVFkZiYiI+PD8OGDWPmzJkcPnyYffv2ERMTw44dO3Bzc6NevXr/8VwKCwtJT08nLS2NXbt28cEHH1BWVkbfvn2xtbUFhQZs3SgWtVi5eXNs21fYuPmQm56EobyCqP07yLp+AiVm7J2dmDp1KqGhoRQXF3Px4kUePXrEggULqF+/Pt988w25ublotVoEQcDGxqbyNf6AUpDhq9BRT25Jnmik/JfiSRrk2AmKZwL2qq3TTwarUJmBDAoK4vPPP+fc+fNMmT6Nswu/pN/8WehlIhYKJbE79pN7IYZzt+8w19aJDz74gC+++AI3NzeOHz+Oh4cHAQEB7NmzBw8PDzZv3kxFRQWdO3dGpVIxZ84cwsLCGDx4MFu2bKkOWJcvX86GDRsYNmxY9TbxHj16/KVrZDQaUSgUPHjwgMjISAYOHIiXlxd79uxBr9dTv3593NzcmDp1Krt372bGjBlERkYSHR1NzZo1uX79OuHh4ZSVlREdHc3w4cP/cJFAIpFIJBLJiyNtCZZIJC9UVV/N37YJuXHjBlOnTsXV1ZXz588zffp0IiMj6dy5M6+88gqHDh0iLCwMd3d3FixYwJw5czhw4AAPHz4kKCiIkJAQDhw4wJ49ewgKCiIoKIiBAwcydOhQ3nzzTUaNGsWkSZM4ePAgW7duJTMzk1u3bv3H8zEajSxfvpzXX38dHx8frl+/zu7duxk0aBDFxcXI5XLatGxGfUcVThWpOFmqiN7zFRZKgeTLx3h08wx5F/egKnuMg70dY8aMITU1lREjRnDv3j2uXLlCcXExZ86cYfXq1fj7+9OgQYM/LEb1RwRBwF6mxE2mwU2mwV6m/N3HV93n+9VXX/HSSy9x4cIFAE6cOFG9yNA0IIAGdXw4tiGcEWpXir/dx4EJ7xF7+CR7dn1Pjx492LlzJ1u2bGH69OlkZ2cTFRWFr68v8+bNIyAggHv37lFeXk63bt04cOAAs2fPJjg4mNWrVzNr1iyOHj1KZmYmAHXr1mXx4sXs2LGDH3744bmD1djYWDZt2vTUvH766SciIyPZtm0bjx8/5ueffyYmJobHjx+jVqtxcXFh2bJleHh40LFjR27dusWmTZuYPHkywcHBnD9/ngULFjBv3jw8PDye+/xLJBKJRCL5e0gBq0QieSGqKthWtTYpKSlh/vz5bNy4kcTERBo2bEjLli1JTEys3rZ55MgRTp06xY8//ohGo+HTTz/l0qVLNGzYEK1WS2hoKBkZGTRs2BA/Pz9MJhP9+/cnKyuLsLAw2rdvz6VLl1iwYAFDhw7l0qVLbNiwgV69epGUlERkZCSJiYl/eS6iKBIfH8+7776LXC5nypQpJCcn065dO/R6PXv37sXJyYmePXsya9Ysbty4Qd26ddGqFDxKjKWsMI+z36/DZDAgIFJRVsLChQvJycmhQ4cO3Lhxg6ZNm2I0Gtm+fTsjRowgJyeHtLQ0duzYQdeuXWnZsiWCIDxVefivMpvNz1ROjoiIYMSIEaSkpNCoUSOWL1/OiRMnGD9+PD/99BMAWq2Whg0bkpeXh7e3N1euXMHR0ZG8vDyWL1/OjRs3yM/PZ8WKFQwbNgyZTMaOHTvw9PTks88+Izg4mJiYGL744guysrIAsLW1xcfHh+zsbBwdHalVqxarVq16amzOzs7PNa+rV69SUVFBfHw8Gzdu5PHjxxiNRkaPHs2VK1d466232LhxI6+//jphYWE0btyYYcOGMXz4cFq1akVMTAxHjx7lyy+/xGg00rlzZ9q1a0doaCibNm0iICDg3zrfEolEIpFI/nNSwCqRSP52+fn5hISEEBUVBcD3339Py5YtMRgMKBQKhgwZgkwmY+TIkcTGxnLixAm8vb0JDQ2lQYMG/PTTTxQWFjJv3jw2btxIdHQ0O3bsoKysjLp165KTk0NcXByurq7cv3+fR48e0aZNG1avXs2AAQNISUlBFEW8vLyYMWMGM2fOpE6dOrz00kvUrFnzL82lquJvRUUFly9fJiIiAgB7e3tMJhPHjx/HwsICV1dXlEolTZs2RafTcfXqVcrLyykoKMBkMuHk5MQXX3xB27Ztyc3NxWg0YmNjw0cffUReXh7bt2/Hz8+PR48e4e/vz7Jly/j2229JSEggLy+vOstY1QomJSWFhISEvzQPmUxW3WO0KnBNSEjg0KFDfPbZZ4SGhtKyZUsOHTqEn58fnp6eTJ48mb1793LixAl0Oh0ZGRnk5+ezZMkSXF1d+e6772jQoAENGzakrKyM77//njp16pCXl8fXX39N7969CQ0NpaysjJCQEMrLy9m+fTtyuZz79++j0+koFo18suErhkx7k1RzOQWi4V/2tK2az/LlywkMDGThwoVcunQJPz8/AgIC2LZtGyqVCo1Gg4eHBwcPHkSv13PkyBGKiopo0qQJoihiZWWFwWDA3d2dLVu2sHr1ao4dO8aqVavQarX/o1WmJRKJRCKR/D7pf2OJRPK3qGoXApXZszfffJM1ayr7dzZp0oSoqChGjhzJoUOHiI6OpnXr1ixevBiDwVCdzdu2bRvp6ekYDAZq1apFdHQ0eXl5uLm5oVKpyMvL46effuLu3bs4OjqSnp7O6NGjadGiBfv27UMQBDp16sSdO3d48OABy5cvp6ioCGdnZ7Zv386AAQNQKv+8V27VNmaozBA/fPiQ8PBwbt68ycyZM0lJSWHKlClUVFRw8OBB2rVrh6OjIw8fPiQwMJDIyEj27t1LWVkZt2/fRq1Wo9Fo6NatG3369MHGxoZFixYxa9Ys7ty5w8OHD5HL5Zw7d46EhAS8vLyqt++2atWK8PDw6kD74cOHDBw4kBEjRhAWFkZERAR6vf53e84+eayqJdDo0aPp168fc+fOBWDy5Mk4OTlx+vRpAJo2bUp5eTmbN28mNTWV9evXM3ToUPLz8zl69Cj16tUjNTWVQ4cOkZKSgqWlJV999RUjR44kLy+PiIgItmzZQnR0NNu3b+fLL7+kTp06LF++HJ1Ox8svv8z48eOZ+f67rN28iYrWDfmmIo3zzkpOWurZq8/ku4pHrK1I5WdDLtlm/e9mlENDQzlx4gTh4eHs3r2bxo0b4+7ujslkYuXKlWRkZBAbG0tiYmJ1xlUURYYPH06tWrX49ttv6d27N02bNmXDhg106tQJtVqNvb09Op3uuX7mJRKJRCKRvHhSwCqRSP5joig+U7Rn5MiR3L17l9u3b1O3bl3Onj3L6NGjyc/Pp2/fvty7d4+RI0dSVlZGSkoKgiDg6+vLggULGDhwIImJiaxbt646OB06dChOTk6UlJQQHByMt7c3e/fuZc6cOYwZM4b169cD0LdvX/Ly8oiPj8fLy4uvv/6aOXPmoFKp/nQeFRUVz8xn9+7dDBw4ELVazfHjx9FoNHz++ee0bNmShg0b8umnnxIQEMDNmzc5cOAAJSUlKBSV9exiY2PR6/UMGzaMrKwsNm/ezGuvvUbt2rUpKytj586d2NrasnfvXqKiotDpdNXVhquCzarnqnL48GGGDh3K2bNnKS0tZd26ddy7d686G1hSUlL9vVXHbt68SVlZGXPmzMHX15cZM2YQERHB4sWLsbS0ZNSoUaxcuRIAGxsblEol06ZNw8PDA4PBgIODA8XFxZSUlNC/f38sLCxYuXIlCoUCuVzO4MGD2bhxI7NmzSIiIgI7OzuioqIwmUycOHGCtWvXsn79eh49esSY8a8R3LcnZW0aMvn8Plr274WToMJJpsZRUFV/6VBwx1TE1op0DhkfUypWLiAYjcbqgHnDhg34+/sTFRXF5cuX0Wq19OnTh/LycsaOHUtBQQGZmZl4e3uzcOHC6t61P/zwAzVq1CA0NJQZM2YQEBDAuHHjAP72KtISiUQikUj+M1JbG4lE8pfdvXuXffv20bZtW9q0aQPAgQMHWLZsGa1bt6Zz58506NCBd955h4SEBJo2bYper8fCwoKYmBju3LnDrVu3ePXVV6lXrx4bN26kb9++nDp1ivT0dGbOnMmCBQsQRRFHR0e8vb3JzMxELpczbNgwJkyYwMyZM3n11Vfp1asXBQUFBAUFsWzZMnr37k1qaupzb/29c+cOUVFRHD9+HE9PT5YuXcrBgweJiYnh2LFjFBYWkp2djbe3N/Xq1SMlJYWHDx8SGhrKhQsXWLp0afX24Pz8fFQqFd7e3pSWlpKWlsaECROYPXs29evXx9HRkUaNGqHRaHBzc2PevHk4OjpWB0lVFXuftG3bNlJTU+nbty/+/v68++67XLt2DZPJhJWVFR988AGBgYHcvXuXSZMmERgYyNKlSzGZTNyJi2PN2rXciLnGhm82UtPNjfDwcFauXElOTg5ms5nrsXe4X5THkLYdcffywtLSkiGDB7N4wULs7OyoVasWOTk5ODg4MHnyZKytrenRowcqlQoLCwtMJhN6vZ5OnTqRmJiIUqlkzZo1nD59mvv37/Pll1+yefNmZs6cSdia1TCgHQWiEXuUyH8THJpEkQrMmBCRIaBCQF9cQpaFjNLiEs5N+whnJ2cGvvs2n70ynoDunTnw1Qbys7Ix6g3V26rnzp3LvXv3qhct8vPzqVGjBm3atCE+Ph43NzecnJxYsWLF3/BukEgkEolE8nf4o7Y2UsAqkUie282bN3nvvffIycmhR48eODg4MHjwYDZv3kxkZCQWFhbEx8djZWXFtm3bCAkJ4fr167i6umJtbY2dnR0W1nZcPH+GNh26YNaXk5eTjSAIpKWl8fXXXzNhwgRsbGwAKCoq4r333qNdu3bk5ORUV9FdsWIFS5cupaSkhAkTJuDu7s7OnTvx9/enYcOGf2kuDx8+pFevXqxbtw7nGjWoUcuTx3l56A0GZGYz7y/4gEmvjK6+f9bPz4+YmBjs7OyIj4+nvLwcjUbDrFmzSE1NZfPmzWzdupVhw4ah1Wrx8PBg9+7dLFq0iDNnztCkSRPCw8Oxs7MDft22+9tA9dy5c6xduxaDwYCLiwvR0dHMmzePyzHX2H/wAG99thC/wADO7zvI9hVrKCstxcHBgfT0dL4+FMnBzES2LlrK41vxWNRwxMGvLt6ubhz4aBlWOh0BLZpz4/YtardvyaNbsWTGJaCx1uER0JAxW79iY+9XeRR7n7179tC1Sxc+/PBDrl27RnJyMnK5HEEQmDVrFnXr1kWn01FWVoaTkxPz5s0jKCiI+vXrs3v3bg4dOkSfPn0Y8XoId3xrYMSMjfD0tuwy0US2WU+mqKfybIg8uhDNtXXbsG9Yj6ZvjcUsE9harwtaZwe8+nclPvwHSrMe02L4QF5a8iGhfu0xlpbh4+tL5zZt2bRpEz4+PvTv35/Vq1ej0+k4ceIERqOR/Px8Jk6cyJkzZ9BoNP/Re0IikUgkEsnfQ+rDKpFI/pIKk8idxyZuZJspMYBGAUdXfkPzoGA+/GAuGSUiBaV6CmRKCnU1eVQsYmXpgNnSmbj468yY8z5arZauXbsS2DyIUqUt3639ErOYgr1PM0pdm5OddJv713+mdh0f9Ho9p0+fZvbs2WzatIlt27YxY8YMrl27Ro8ePfD398fS0pKYmBiOHDnCwIEDmTt3Lnl5ebi7uzNs2LA/nEt+uZkfE43cyTFTYRIpvh3Fya/m0K9XN3788UcSUlOIK8zjRnIiV27cpEf0EYylZZzqP4axQ0eg1mjQ2djQqVMn3nrrLQYNGkRCQgITJ05k7969JCcnYzabefnll9m5cycbN27kpZdewt3dnZSUFCIjI9m0aRN6vR4rK6vqcVUVQqpiMBiIioqia9eu7Ny5k9OnT5OUlIQoiixY8hlbzv1MiY0F8rqe7D4bxdHkWLaETMPC2orvLpxEn5DKst3b+ErIQu5ujT4nn9K0DJq8+hIXwtZzq7gElY0V9V4bzs3Io4gqBXcO/szU8HVc/fEQsacvcPfwSZL3/0zN4GYUFBay5sAeOnTuxOnTp7ly5Qrbt29Hp9NVV9L19PR86lw3aNCA9PR0pkyZQmBgIFOnTsVPl5ShAAAgAElEQVS3QQN26jPQiwbsnghWzaJIqrmcdLECAQE1Ag9PX+LsZ19RnJ+PXGdBo/lTKFZAYVo6HgO7knHsPHfX70DraIdlo/oUyUTC2g3ANyiQmEM/Y9eqCcHTX+fkyZNkZmayYMECXFxcmD59OpMnT6Z58+ZMmjQJf39/Lly4QMeOHf++N41EIpFIJJK/nXQPq0QieYooipxKNTL/bAWbbhm4m2smvcTMmYvRnDh9ljj/ibx2sIzPLpTz4Xkzr4bHsv1CKuUoMVSUMfHbswQMnMjpqCjiHqTTttdgvtm+h9KGL+HVrCN2zm407zaIuwc34uMfgFylxrP3ZAwouHo1mlWrVpGVlUVSUhIbNmxApVJx9uxZAGrWrEnt2rU5ePAgvr6+fP311/8yo1pqMPPRuXIG7C3jq2t6TqcauZxu4ujlu8TfuUFkbDFH7mXzramQMzevo2zggyCTk3/iItYaK0xFpWAw0iJ8JRr/emzeto26vr5YW1uj0Wiqq8/Wq1ePzMxMGjduTEBAAMeOHSM4OJjZs2czadIkZs6ciVqtxsrK6qm2NFVbgav+npiYSP/+/SkqKmLEiBF4eXlx+EwUUYY80lr4EHP3Lo+u3SLx5Dmu7/iBw/O/QK3V4OLjzd4rZ1mdfpucwkJUWQXoyoxYONihsbOlPK8A0WxGpbPAf9xQ4o6f5nHiA9z96lOriT8VpSXcOBqFWmdB/XatCJ85j/5TXsevdRD7Vn9D3cYNKS4uZvr06Zw/f545c+YwY8YMHB0d0ev1JCcnk5SUxPTp09m2bRtdunQBwM7ODj8/P66ZCsk0V2D7xBqpKIokmct4JFagRYaFIEMuCBjKK8hNTqHpgrfJPneN+DXbyboVh8xaR2FcEn7TxuDSphlNxryMaDByf+9hjAoZg1Z9ikwu5/KmCPYfP8K9xAQ6duzIhQsXuHr1Kl26dMHDw4MVK1YQERFBWVkZXl5eL+Q9JJFIJBKJ5O8jZVglEkk1URTZn2DkaLIJF0tw1P66pqV3csQkU5OSU4xMp4GiLBJ3fUzO1QNo7N0wlJcgWDoQuXgqOQk3MVaUYl3Tj03nHjBkxQm8XWy5Y2GB4O5FWlwMNs7ueDVuhbGiDP2jOzQb9QHmjFvMGzUKrVbD8uXLOXToEM2aNWPXrl388MMPdOzYkd69e1OrVi2A6m21v6dEb2bq8Qru5Jhw1IJK/utcLNr0IXnruxQVFzFpxWK8x/bAqXkTTAUlaJ0dSdr5A87Nm6KwqqwWW3z1FrranhTE3afT0MHExcUB0LNnTzw9Pbl06RJbtmxh3LhxfP3113Tv3p158+YxaNCgp86t2WyuLuZU1S5nyZIl+Pn50atXL3x9fRk4cCBhy5bx0vvTsQxqxMd7t9Gg6VvcOXeJe8dPU29oH9qt+5jrSzdSr0UT0mPu0GxAT3bPXIDZZKQ8K5cH+4/h06sTtl41ST17hXs/HqM8Nx+NvS3Z8QmYikup+8Yw0i/foEXrNpwKj6Bhp7ZorCyRKxTEnj5PUvQNxn/1BU61a+E/ZjAZW/YR9skXrFy5knfeeQdbW1sKCgqwsLAgKiqKtWvX0rFjR06cOIGjoyNQGZBXiGYuGguwF5RPFTRKM1eQJeqxRPbUcasuLTEbjDg38UNla839HfvJuXuP5svnYhvYgKSIA5Rn52Jpa0P9wT0589EK8mITWNy+H7ZuNchLSycv5i46F2eUzvaMHz+eBg0aULt2bcLDw/Hy8uLnn3+mqKhIqgYskUgkEsl/ASnDKpFIqsVkmTiabMLDCtSKJwriiBBfIEfj4Ych7iQqGaSXCtg17kqXLy9jLCtEoVJRcP8qt88eRq+vwNrRlfzsRzw89i13bt8iLTaGrOQ4fJp3JDH6NL6tenDu+7U4eNTBp3kHmga14ta1K8x5fx7NmjWjtLSUc+fOMXbsWLp3707z5s2ZMGECDRo0wMLC4k/n8unFCu7mmnC1fDpYBZAplTj5t0Wfcw/blnW5Ou1DVNY2uHfvgK52TR6dPEPCjr2YysoQ5HKuL1pOrR6d6XJoJ/K+XTh5/SoVFRX8+OOP6PV6Ll++TFlZGYsXL8bPz4+UlJTqYNVgMACVwVtVsFrVyqbq33ft2sXjx48BGDVtMmvDN3FMn42thxvRW/dwe9Fa7u/Yj9lgxK9vV7xaBBI4czyXIw9iWa8Wp3ZFYuVbG6taNRFkMuwa+ZJ88jyNXhlIQMgQynLyafrGK5Tn5pNx4gI+w/rSMnQaJdl5GB2sSI9PoDS/AH1ZOVd/PEwNHy9OfrcDgD7TJlDb2YVaowdhYWFBq1atsLW15dKlS4wbN47o6GiGDBnC6dOnWbRoUXWwWiXBVIoREaXw6zUwiWJ1ZvXJYFUvmjEIIkGfzGJ7nY7YNfLFpmE9fKeO5sq0TzCX6VE52dNw9usUpmfi3aMDNdsGoXWwoyK3kDazJmLt7ISlrQ364hJ2frMJURS5dOkSgiBw8OBBQkJCCAoKIjw8HAcHh+d6X0gkEolEIvnnvLAMqyAINYHNgAtgBtaLovilIAgLgNeB7F++9X1RFA+8qHFIJJLnI4oih5NN2GlALnu6emt+hUih2glb19pk3zyFsX5vdHZOCPb9UVkJODfpjFPjTqit7MlOiafD4Ne4vWU+107+hFtQbxJORnA34QKtBoZg5+pJ37cXE3fhCJ4Ng3j8MIHjGxdTXlxAw56jMBZms3TpUrKysvjxxx9p3bo1kydP/ktzySg2E5VqxlELgvDsupzaxhnHJp2I3fER9sGB5F6I4faytQSHLcS5ZXPK0rNI2hWJ77iRPDx4nMwzF7CqUxvrmh5oarpwz8IWX19f5HI5n3zyCVZWVmzcuJGQkBCgsoDS7du3CQsLo0aNGkybNo0aNWpw79493nzzTQoKCqhbty6tW7emT58+rFy5EqPRyPiPPySusQcKtZqlzXqisLRAbWNF0pHTdPr4HS4sXY9MoeDh+atcW7MZQ2ExOWkZmOx1tPvuc4qu3CHndjyPY+5iodNRmJaBc2M/6vTsQKcl73P9u+9R29tiKq+8Z7T2gK6VvVRtbSgpKCQj6QEtB/djwOy3mFSzMRWlpagtLDCbzagd7ZjwwXt8+eWX3Lp1C4PBwMSJEwkMDPyX1+KeuRT1b9ZGc0U9IiLy31ybYkzIEKg9qDvKt0JpH/kVkXW64tKlFY/PX+Pe+h20WP8R7h2DyTpxifi9h6n/Uk90rk7c2fUTt46cpOukMfz0xRo6jBnO1UPHaNiiGZnJKXh6euLn54enpydvvfUWarWamTNn/qWfK4lEIpFIJP/zXliVYEEQXAFXURSjBUGwAq4CA4GhQLEoil8873NJVYIlkhfvQaGZZVf0uOue7UV5I8tMZqkZWVEGN7eEUlRUhHu/GRQ9jKPs4nYs7GvQeNznKC1tMSFgMEPGno+JO74De59APPq+jTIvgdTD6/HwbUqfqZ+i1GgB+OGLGeSkJfLash8QRZEHeQYaZh7At6YznTp1+rfmsiamgvDbRlws/7inZklZEucXDUWs0NNw6WLSI3+k8PwFnIIC8ezXA5lSgVPzpuTeusvPw8Zj0utpt34ZeUlJ3P18NRYyOVEnT5KRkUG3bt2AyqBfr9fz/vvvc/jwYV599VWCgoKwtbUlMDCQb775hrS0ND788ENOnjzJyJEj0Wq1+Pn5UY5Igxkh1Pf3Z3G3wTxOSWP6w8sc+3QFN9ZvY8DmZWTfiiPm2wj0hcW0nz8NjZ0NBybPw7FrK8wlZTSdMZ7HV25xZf4yanVvR5/locTuPUTuvWS8X+nL7b2HiF+3A+s6tWi1bC4F95K5vX47Hm7u9JkYgn/HttXn56dlaygrLOLl+bMBOBmxF61CwRcvhxATE0OLFi3+9DqIosi6iodokT2VYb1pKkIvmlE9ccyMSLq5AgUyBODG8o0UFxah1FkgGoxY1fHkwa6D2DauR503XyX/1GUefhdJQfJDSnPyUFvrKMvNZ+Keb/Gp7cX1Qz+zbe5HuNeuzd4t29i6dSsxMTGkpaUxYsQIpk+fLlUIlkgkEonkf5F/vK2NIAiRwCqgDVLAKpH8r3PxkYntsQY8rJ4N8o4/MKKWg0wQyM3L5+6+NRgz4zEU5+E1YDp1mnVAFEWKHsaSdmY3Dy/9hM7ZE8+eEyiKv0jhwzgMpYUMHPc2dYM6P/XcJzaHoVRraDtsMmaTifRSGaP9lTStIf+35/LG4TLu55mw1fzxXQ9Gm2IK7W5zOrgXzt27YigqotGksbh3aPXM9xbcS+SnDv1QWOkwFBahtrPl+7276du+MqCuKqQkk8mIjY1l5syZ7N+//5lWNT179qRfv35MnjyZ2NhYXn75Zdq2bcsnSz5n0NsTyXuUQenjXAwVegL7dKPOB5PIvRnHybmfo7S0oM+6z9jUZhBaRzvGnNwFQPz5yxjqe5Kwdjsl8Q8oiktCZW9Dv31f4yBTkZ+Uyq3tkSScPE9pTi5Fiak0nDqG4pQ0KvKLqPvWKHp16YqHTPvUWPVlZUzxbka/WZOJ+m4HOgd7Bi9byKKWPZ/7OlSIZtZWpOIkqJ46ftlYgAoB2RMLI0ZRJFOsQPlLNraivJyIel2oP20sN0NX4dIxCLfeHck6cxVkAhobK9QGM7mxCVi518C+rhcXwtbT5OV+9Bk/mtPhuyjIy+XxvSTS4u5Xnqv4eOrVq/fc45dIJBKJRPI/5x9tayMIQm2gKXCRyoB1iiAIo4ErwExRFPN+5zFvAG8Az7RNkEgkfz+DWYQ/WL8yiVAVWsi01tQYMAeFsQSzwhKrX2IRQRDQ2ruhsXfFb+IanHyaAODetDMVJUWIGh11PZ/9yFFbWKG2sKwM+ORyQMT4H66j6c0AImajEZni9z/mRJkZTQ1HPEYMwaZZU2r07oW99vczsjZ1vfGbMp7M85cJCJ2NQ2AAzZ28MJvNCILwVHuaxMREHBwcMJlM1Y83m80oFApGjx7NV199xeTJk0lMTMTe3h61Wk2C2kTvz+fx6PBpagc0JOFKDMfWfUdtJuHU0JfGY17m0aXraGysqNOzI3GRRzg2+1Myom+idnFC3bAOPuOGILPQkPTlZjRO9gDkJ6eic3Wm7ftTsO8YhNbXi5uL1uAc1IQms15HqbOgDBOm35mzSquly/hR3DwWxZvfrqJ2YGNyMfyl62D+nR8oURQx8+vPU/Xx3/xdX1iMW5+OZEVdxtLTFY2rM3UnDCf/VjypkcfwGT0IZbkRhVZN1o1Y8hNTqdW1Ldn3knicnEqTHp1IvHkbpSAjJaVyS7AUrEokEolE8t/nhWdYBUHQAVHAJ6Io7hEEoQbwmMrfTz6ictvwuH/1HFKGVSJ58a5mmAi/8/sZ1qhUEyCilAkU6UUyS0XUctCbwE4D9k9lMkWK9GCjFig1iGgUAnqTiEIm0Nbj2axpVbXcKmlFIiGNlDR2+vcyrKIo8vbxUmKyRBwtZFQUVhYzUls/XQzIaF2Cvn4m5nIQBDlmERwtBOR/sIu4apx6wYzCKLLC2QdHhfrXcaelUVBQwIEDB7h58ybfffdddUD75PwaN27MhAkTePDgAdu3b+f9uXMRRvdGLZOj/mWLrCiKvNeiK3XHvUztDq049cESvLu1I/CNkZhNJqLXbSXhcBQdFkzHoFVzavFqHAL88Br/MueHT8ezb2fyLt/EkJFD50VzcG5Un3zRQKloRm4WEZ7I/JZhoqncGnfZs9tjzWZzdSCe9ziHtAcP+LR1n2eu2R8xiGa+qkjFkacrBF8xFqD8gwyrQhQwG4w8OBSF4OqAITOHq7MXI1ep8Bo1AJ23J/Grt2Au19PhvSkcfXs+/iMGIJPJCHh3IkUHz5J04AT5GVkM/GwuQYHNGKBy/tOxSiQSiUQi+Wf9UYb1hVYJFgRBCewGtoqiuAdAFMVMURRNoiiaga+BoBc5BolE8nzq2MmQASbzs4tYnlYC5cbKP2sUldkx0Vy56mSpfDpwqTCBtUqgppXwS6az8pj7H3QQeTKQMZlFBAG8bP76R5PZbK7+cy9vNSW5WVwOG8Op97uRe/ci8Gu/UwB5uRqxQo6gFDCLIJfxh8Hqk+PU6yvwVGuxk1emltPT01m5ciWvvPIKZ86c4ZVXXmHfvn2kpaUhk8mqx5WSksL9+/fZuHEj6enpHDp0iJycHFr37o5eJqL85bznZ2SRmZjMxG++xJyRw4/j36FmmxY0CRkKgEwuJ3DiqwzZvQ6Xpg1x9fXBITiA4gcPKbgeS8aJi6TsO45nmxYM//FbnBvVB0CNDDNPB6smzAgI2KH83TnLZDLS7yWw55OlfPXaVNYPGf/UufgzSkGGraCg4jf5UxtBSQXmp47JBQEBAVEAuUqJe4cgTGXlJGzeS7uIFdgH+hG3MpyKvHwsPF0xFBRiKimlRuMGIIoUpKRx9PX3uLZ1Dz2mjGfu4V04B/jhLlMjkUgkEonkv9cLC1iFyt9ovgHuiqK49Injrk982yDg1osag0QieX62aoGAGnKyy54NWF11lR8VJrEyy2qhgHIzqOWVX78SKTOCl41ADUsZcgGMvwRsVc/xrzwug2Y15Fipni8gAjAajdVbck0mE2azmZt7vuT2h23JvnseB782PDi5lfLcjKceJ+gVyAo1CDIREfGZwPu3RFHEhIhcpaK/1oG4u3dZtWoVU6ZMYdmyZaxatYpx48bh6upK9+7dWbx4Menp6cTFxbFmzRrmzp1LREQEzZo1Y8GCBdy4cYP+/fsTtmQJ+elZv2yHhtsnznDx+/3UauzPyND3GHZ6F0Fvj0OuVFYH3DKZrHqrs0IQMGXkoHargVNwU1ptX0r3/etpPnboU+PXCHJkCIhPBI8GRBxRYiGTPxXwVynKyWXNa2+jVKvo9u5UMJgIDw9/7msDUFOmofQ3m45dZCrM/NqbFioXQXTIKM0v4MSYWSTs+JFHe45SGJtI8tb9PL4Qg6WnGy492uHRsz2Wrs7c2LKXtnOnoi8uIeP6XbTWOkLCFuLToinmX85VfbnlXxqvRCKRSCSS/11eZIa1DTAK6CwIQswvX72BzwVBuCkIwg2gEzD9BY5BIpH8Bd1ryxGo3Pb7JI0CGjjIKNZXBq3W6sr7Wm3VUHU3oohIQQW4Wgo4W8pQyKC2tUBWKdSyFtAo/nVAWG4UMZihQ83n3wosiiIKhQJBENi3bx/jx4/nzJkzfPH5Ymq6u6Gt2QivQe+gtXPhwfHN6AsfU5D86xqZMssKk0GGUiWiVvz+7REm4y+pZQEqBDP5ET/Rv5YPL730Et988w0lJSW4urpy9erV6gBsyZIl5OTk4OXlxcCBA5k9ezYHDhygXr16CIJQHaQtWrQIs1zGN0NfZ/8XqwjtNIBj67/Dv1PbynuCqcxQlpqMz2zD1ReXcCFsPd+1f5mi67G49euECTOufTqgRf5MFlQAdIIc4y8Bq+mXO0xr/1JsqWrr791T58l7VBncZ9xPoqZ/fTrNmEjTVi1ZtnQpy5cvf+7rA+Art8T0mwxr0rEzyMv0GKhcaKgoKib7zj0sZQrUttakHDiJIJfj0SkYY1EJZelZtN+7Bvtm/uTfiMO5RWO0tjbkJ6Vg6epEzxULGXo6gnHrwnCr6wNAAUbqyizQCf8jpRokkv+v5ObmkpSU9E8PQyKR/H/ihf1PLoriGZ6tqwEg9VyVSP6XcrGUMSlAxbrreor1Io5aUP6yT9bTSoYoisRkiZhEgWZOIumlAoUV5sotwoKAu06Gv6MMmQDFehGFXCCwhhy5IGIyi8/0d61SYhDJKRMZ7a/Ew+qP19GqAkL5L9lIQRCIj49n0KBBFBcXM3LkSL7//nuaNm1KeXk5irJScvMKKNUbSf5+CQkH11F/yLvoXL0pRUNRsQZXKzsUXnnoMaNGhsAvAaXZjEyhQK5QYBJEys0msnfup3Dz97QMCqJVq1bs2LEDKysr7OzsuHnzZnUBpjVr1nD69GmcnJwYNmwYNWrU4MqVK+Tn5wOgUqkQRREvLy+mL/4Yu9PHyL5wjZfmzqBR1w7V8xUEAW+ZBXcUIuWiGe0TH6kKjRqlzpIeX4biEuBPhlhOASbkgI7fD/otBTklookK0YiByuyns6xya3NyzE2OrvuO2yfOUL9tME17daFmw/qkxd6jyKSnq6IG3q++ypuTJrF//3769ev3XPeyugpq7AQlxaIRnaDg9JZdrHt9Gr3fewuv2a8hU6iIHDOdivwiGo8eTO0xg7BrXJ8H+4/TaXMYOu+alKZlYu1bG0GpJGbWZzSdOxm/QT2xmuiEjYcb5aIZGytrHGSVW5vLRTMmRFoobP7l2CQSyb8nJSWFZcuW8d1337F//366desmtYmSSCQvzAu9h1Uikfz38baV8U4LFW3c5eSWVxZBSisWSSs2o5TLGNtQyYzmKvydlPg7yLBVC+hUMrxsZDho4VGxyMMiEbkg8GoDJcs7q+nsqSSjBB4WiZQYRAxmEb1JJK9c5GGRGb0J3mispLnLH6+hVQVHVcHquXPn2L17N+PHj8fDw4OVK1eyefNmDh06ROvWrXn48CFejpYofpyD6cFl5Fodcisn8goKyDZq0allTA1UsbmpM2/oXNEJCh7FXKfEUI5RDialjArBzJl3PuBI31co2vUTnQxyOrRqTXJyMrt376Zr1660adOGiooKjhw5QqtWrdi8eTO5ublMnz6dzp0789FHHzF58mQCAwO5du0aKSkpT83LVqHGp20Q/d+ZWh2smp+oMKwSZPjJLLEQZJSIJspFc+UWaIWCZhNG4hzgRxlmdCiw/+Ve1HJEfq+enmAGrVlAL4CToKKxoEMQBH7esIXPB7xK/bbBLI+9QPMBPfn5my3Y+tRGZqkhf9N+vJU6CgoKaNq0KZs3b66+Jn9GJgh0UdpTIpoxiSJypYL2o4Zy7+R5fh72NmX6cgKmjEFrb8vJ+WHwMAutvQ0aN2dUNlbUHzMYQ34hx7qFkHnsHDa+3tTr1IbGIwfh1aUt5aIZOQK+MgsUyCgTTRRipK/CCSeZ6k/HJ5FI/pzZbH7qtgEXFxeOHDmCp6cn0dHRlJeX/4Ojk0gk/9dJe6UkEskzHC1kvFRPRk8vBalFZipMoJSBu06GtfrZjFpOmcj9fDMlBhGVDGpYyqhj+2sV2EH1ZHSupSA608T5RyYKK0TkApxeNZ2xI15mVL/O1ZncPyIIAmVlZYSFhREZGUliYiLffvstu3fvplGjRgwbNgytVkufPn3w9vYmMDCQI0eOIJfLGTduHFu2baM49wE+pbdolriWcUP7Ut/XF4AuShs8C0sIu5mEuZYXpZYyzk59j9xrNxCMJtxrOHPlg0XoOnbk3Llz6HQ65s6dy4gRI/joo4/4+eefkclkqFQqpk+fTmBgIOfPn0elUhEcHEzfvn2pW7cu69evp1OnTnh6elZnJu2FyiDzyV6uVfezVqkMWnUUYSLDXEG+aACx8vEC4CQocZarwWwmHT3liOSIejBXfsiLoohJqCxo5KBQ0aHUgh+/Dee4iwNBQ/rTZtQQDq76GpVGjSiKeLRujmbvj5z/fj9hi5ewZ/V6OnbsiNFoJCQkhEWLFvHo0SPc3Nz+9GfJZDLhLtcQKNcRbSqiorSMWz+fxqGmO8UZWWzx687wo1vosHoh21oO4PSnq8iNuYu1T20qMOHQsQU17yejsrHCd+Y4NMhwREmZaMaMiE6QU09uiUkUyRIrUCFjkNKZWnLtn45NIpH8uSfbdqWnp2NlZUVGRgbBwcHk5uYyf/78f3iEEonk/7oX3tbm7yC1tZFI/m8xmUzI5XImTZqEl5cXs2fPfubfgepsKsCdO3dYsmQJ+fn5BAQEsHPnTjp16oRWq2X9+vX4+/vj7OzMoUOHCAoKolu3bixcuJAaNWqgUqlo0KABffv2pbCwkNu3b2NnZ8eyZcueeZ1de3Yjt7Tg/RmzMJtMjHr1Vc6dPYsoiuTm5mJpaUmrVq04ePAgMpmMBw8eYDAY6N+/PxkZGXh4eKBWq+nWrRtnzpzh1KlT2NjY4O7ujtFopHXr1kyePLn6F0BRFAnXp6MXzVgIz3f/rvGXAlAACgTkvwS/2WIF7RT2BMitiDUXczglnvvx9/BpH4yNoKClwoat8z9jV0QEw4cP50Z8HM6B/jR6eyw/LfiCvNRHjPxmKc6igsLD59i6bBU/Hz+OQqHg+PHjdOjQgZMnT/LDDz+wfPlyFH/Q47YqEyN7siKxKHLU+JgDZ07z/cTZfHLhMOd2/sA3b75Di0F9GL91DUd3fM/1yIPc23cMtY0VM5LPo1IoSc/Owuxgg0oQ0IgyigQTNoISO0FRfc5sBAXN5NbUlVugec7zKJFIfl9YWBjOzs6MGjUKURRJTk5m5syZZGRkEBAQwJgxY2jevDkdOnQgNDSULl26PNUGSyKRSP4df9TWRsqwSiSS/xG3b98mISGB/v37I5PJMBgMaDQa3N3dn/q+J+9RjY2NRS6X8+jRI6ZPn05OTg7NmjVDqVTi4+PDsWPH0Gg0qNVqLl++jEqlwmQycefOHaZMmcL06dPZu3cvY8eOZd68eQDs2rWLkydP0qNHj+rXOXLkCJ9//jmjRo1i586dqNVqgpo1QxAEPGvWxPvVV9m5cye3b9/G0tKSS5cuIYoiFRUVNGnSBKVSSWZmJr169eLSpUvExMQwZcoU2rZtS1xcHBcvXmTs2LEUFRVx9+7dp36pEwSB5nJrDhseo0X2XC1jFIKA4jclAgyiGRkC9eQWiGYzfnIratdoQIehExm8eDE7duyg98KF1PH2JiwsjMDAQAYPHkzmgxRebdOZwe8uoH2LYLqlVtDAx5PM4HZE1z/GnTt3aNy4Me7u7nTp0gVBEJg9e/YzwWpiYiIbNmzg008/rZ7ftWvXuHDhAps2bWLSpEn0GzCAyPM3KMovYJpfa8x6A66+dSjIyOL811voPmwQLZoGEhp1kbavvNtTDx4AACAASURBVIyvzJIKmQydsxt1ZFqCFDZoBDkiIiWYMIgicgS0ggwnQfVUX1eJRPLXVQWd9vb2fPjhh4waNQpBEIiIiGDIkCGMGDGCHj16sGTJElasWMHAgQNZs2YNXbp0kYJViUTywkifLhKJ5IXT6/VcunSJiRMncvfuXQRBQKlUotfruXjxIqIoYjAYgMqsXHR0NL6+vrRp04a5c+eyfft22rdvT1lZGenp6dStW5fs7GwePHhAYmIiRUVFKBQKTCYTKpWKli1bsmPHDpo3b05hYSGRkZGMGjUKLy8vRo8ejSAI9OjRg+joaPr27cuoUaPIy8tjy5YttGzZktLSUnJycvDx8SEpKYn09HSio6PR6/UUFxfzySefEBISQrt27SgoKCAtLQ2dToeHhwepqanUqVOHmTNnMnDgQFxcXDh16hT29vaEh4fTpk2bZ86Pr9wSF5maQoz/1vkVRZFc0UCwzBqdoKgOxC0sLMjJyaFnz57ExcVRXl5OSEgIZ8+epWvXrsyYMYOgoCAOHziIu60DHdq149PQhQiCgIuLC+vWraNx48YA2Nvb8/HHH3Py5El69+5d/dpmc+U9tS4uLqxdu5br168DMGDAAMaNG0dqaipvv/02V69eZfnSpcwdPZ6ynDycPT0Ys2MNc64do9GAHlz96Qix5y7h4uvDorgL9F7yAbkKMypB4CWVMwNUzrjJNdjLlDjIVHjKtNSRW1BbrqWGTC0FqxLJf6BqV0tV0BkSEgLA/v37gcqtwAcOHKB79+6o1WrmzZuHm5sbISEh3Lx5k3fffZdRo0YRFxf3z0xAIpH8nyZtCZZIJP9j5s+fT3JyMi+99BIDBgxg37591fehymQyrl69SmZmJlOnTqV27dq88cYbzJo1i7S0NCIiItixYwfHjx9HJpOh0+lIT0/HbDYjCAJvvfUWgwcPZs2aNWi1Wuzs7MjNzeXy5cuUl5fTtWtXXFxcmDBhAtnZ2dSvX5/58+ezZMkSgoKCOHXqFG3atMHW1pby8nJUKhUajYb09HQ0Gg0PHjygqKgIb29v9u7dS15eHi+//DKZmZkMHjwYjUaDVqvlxIkTtG/fnl69ehEUFISNjQ0ffvghSUlJvPnmm7Rq1ep3z02O2cBOfTpKZFj+hS2toijyGAMeMjUDlTWQAZGRkURFRZGdnc3NmzfJzc0lODgYGxsbNmzYQKtWrUhLSyMlJYWFCxeybds2Nm/eTO3atYmJiaF79+7Vz1+1ffv3VJ37qqzw+PHjKSgoYNeuXTRp0oSUlBQOHjxIcHAw165dq854t2/fHkdHR7oN7MelWzeIPneBrnOmEDC0HwoEHGRK3AUN3nItroL6ubLOEonkP3f+/Hlq1qxZXchuy5YtXLx4kTfeeIOMjAwWLFhAYGAgAAkJCdSpU4eDBw9y5MgRhg8fTsuWLf/hGUgkkv9mf7QlWL5gwYJ/YDh/zfr16xe88cYb//QwJBLJnygqKiIiIgK1Wo2joyPwayVZQRAIDg6muLiYsLAwOnfuTFpaGnq9ntq1a7NixQqmTp3K+PHjKSwsJCcnh5UrV5Kfn49Go+H8+fOEhISQmprKvXv3KCgowNfXl+Li4ur7V2vWrMnDhw9RKpWcOXOGa9eu0blzZ1xdXcnNzUWr1fLaa6+xY8cOzp07x9ixYzly5Aiurq4EBASQlpbGuXPnqFWrFidOnOD27dukp6dTWlqKi4sL1tbW1KpVi6ysLNq2bYtcLkev1yOKIkeOHCEzM5OwsDDGjRtHvXr1UKlUCIJAq1atGDp0KDVr1vzDc2chyKkp03DHVEwpZjTPsT3YKJp5LBrwkGnop3KmKC+f0aNHc/HiRQYNGkRmZib9+/ensLAQa2trHB0d6d69O9nZ2Rw7dozk5GTu3btHnz59aNeuHa6urtSpU+ep1/jtNr8nFzllMhkpKSmsX7+ecePGER8fz7Vr11AoFPj6+pKbm0uTJk3w9fXFysqKhIQELly4gEKhICgoiPSUVBrXqcfWjZsY3Kw1LRW2tFDY0FBuhadci5WgkIJVieQFeOONN7C3t8fT0xOozKT27t2buLg4tm7dSmBgIP3792fevHn07t0ba2trcnJyqFGjBh4eHkybNo19+/bRt29f/Pz86NmzJx4eHv/wrCQSyX+70NDQ9AULFqz/7XEpwyqRSP5j+fn5zJkzh+joaJydnamoqGDChAkMGTIEqMzSCYKAXq9Ho9HwzjvvUFxcTH5+PteuXavuoXr9+nWGDx+Or68v3bp1Q6fTYWVlBcD9+/epV68emzdvZujQoeTm5jJnzhyWL1+OSqXi8ePHNGrUiK5du3L06FFKSkoYMWIECxYsQBRF/P39KSkpYf78+Rw9epSLFy/SunVrvLy82LdvH9OmTft/7N13fE33/8Dx1zl3ZE+yJLFXiMSIooLaoWhV1Yr55WvXrBpt7do1iqrWjD1CkZq1V2ILCTEiJLJkz7vO+f0RblE60f76Pc/Hw0Med5x7Pif3ntz3eb8/7w+BgYHUqFEDBwcHtFot+/btY/DgwVy4cAGtVounpyft2rXj9OnTLF26lL1797Jz507mz59PdHQ0nTp1+svHMls2csSQTqyUjwYRK0lFoVHAKBUt66MWZTQYyNcISLJMfbUT4d+t5/TJk9y4cYPCwkKCg4P55JNPmDp1KpcvX0alUrFz506Cg4OpWbMmPXr0oG/fvpw9e5b9+/fjU80XHRISoEUoWo/2uUDxye/w6QC2sLAQa2trXFxc6N69O3PnzqVu3bqEh4fTtm1bNBoN8fHxhIeHA3Dy5EkGDhzIjBkzaNOmzV8+VgqF4veTJAmTyYRGoyEjIwM7OzvUajUGg4EePXrQr18/mjRpwqRJk8jOzmbs2LGsXr2aEydOsHv3brZt28bmzZu5desWzZs3Z/To0bi5uf3dw1IoFP8iL8uwKnNYFQrFnxYVFcXmzZu5dOkS3333HQcOHCAsLIwmTZpw4cIFMjMz2bVrF507dyYjI4PY2FhycnJwdXUlLy+PCxcuEBcXhyRJbNmyhZiYGOzs7DAajeh0OgoLC3FxcSExMRG1Wk1sbCxHjx5l2LBhGAwGvvzyS4YNG0b58uVxc3MjLCyMoUOH8uOPP3L+/HlSUlLo3bsPmZmZ1KlTB5PJxNGjR4mIiMDX15eTJ09Su3ZtHj16xLhx4xg8eDD169dHFEW+/fZb9u7d+0wpsLe3N9nZ2fj7+zNgwACWLFnCzJkz8fPzMwerf/UioL2gpq26OA0MbmSkavkpUceptEI2rdzEdxNnc/KRjtOpoEnW0iLbFl+jlnlz5nDjxg02bdqEr68v06dPp2/fvhw7doyUlBQKCgoIDg7m/v37jBs3jjVr1rA9NBQ7J0d2Jdxkqe4B3xc8YJUugW918azTJ3LDmEtadpZ5v1QqFaIosnv3bsaOHUtISAiFhYV07drVnGUGmDZtGra2ttSoUQMfHx+ioqJo3rw5UVFR1KhRg3Xr1tGmTZu/fJwUCsUfI4oiGo2Gu3fvotfr8fPzY+fOnWg0GvLz88nOzgagZ8+eyLJMZGQko0aN4scff+TatWt8+OGHLFu2jPDwcObMmaMEqwqF4o1RugQrFIrflGeQSciR0ZlktCp4dPc6MyaNJz4+npYtWxIcHEzjZs1Yt3snXbp1JaDOWyyY9xXBwcFcuHgRB0dHmrdsSVJiIo9SUxFFEVmWUavVqNVqkpKSEAQBOzs7Ro4cSVJSEmq1mry8PM6ePUvx4sXZvn07rVu3Zu/+gwwcMwnHYi5kpqXyzTffkJqaSo0aNbC0tCTHIPBt6EEWz5qEtXt50m5f5HTCELJvnSMpIYEdO3Zw9OhRBg0aRG5uLh07dsTT05OMjAyOHTtGfn4+LVu2BGDTpk18/fXXLFiwAJ1Ox4MHD7CysmLcuHEMGzbMXAIny7I5I/lnS1gzJAMJso4Mg5GjcSYS0lXY6BzwtXJA1kjs/WoxRl0hgdXfJystk3HzR+PkVZ6JMxcwefJk5s+fj0qlYsaMGahUKkaMGIGlpSWjR4/G09OTGTNmcPnyZZYvX06qLo/VuoeUbNOEn44f4+2G1dEJErKp6I9CVEQEkyZ9RfrdOD7s1Z1JA4eRmZFB3759uX//PiqVCpPJxKpVq5gzZw5btmzh1q1b5rnC3t7eHD58mCNHjmAwGMjIyKBKlSoA+Pv7/6XjpFAofh+j0fhMN+/bt2+zc+dOQkNDWb16Nb169SIkJISgoCD8/f1JSkoCoEyZMkRFRVG7dm1UKhXjx48nISEBX19fihUr9ncNR6FQ/A9TAlaFQvFSD3MlTiWYOPvQhCRTtJCKIHPk628pW7YOG0J2kG6Tww5jOkJwS778bimHPDXcXLGFtBvXCWjVjPptR3F8ySpc/9uXvDlzsfDywr5kSRJPnkCv11OtZg0M+QXodDri4uIoLCykTZs2zJw5k+bNm3P//n1KlCjB+XuZWDgUJzzmIfdCTiCVfwd9ylYSk5Ko6FuT/gMHszNGx8RhfSk0QsVPd2PKSiJhchC3j+8EwKqYJ8W9y3Dl5l0KCgrM82R9fX3NHXCjo6Np0aIFixYtwsLCgnnz5nHv3j1Kly7N0KFDCQoKwsPD45nj9FeCrwdSIeeMWdyXCtGbZG6kS+QZ83l4YhdebweitffBOk2kRvOOpCXcI3TqYNRaCzzLVqawII+Jnw7niwUr8a9+hOXLl/PJJ59w69YtHjx4wMaNG4mNjaVs2bI8ePCAJk2asGjl98Q6W2IrFVLtiyGYBCjAhFouGsOGoO4knruCU+VyuPr5sD1kA1FXI9mxYi3BwcHs2rWL5s2b8/nnn2MwGIiIiKBhw4bExcWxadMmevXqxciRI5k7dy6RkZHMnDlTCU4VijfkSVf09957D3t7e+DnwHXz5s3Mnj2b69ev4+XlxejRo6lYsSK3bt2iYcOGfP311xgMBipVqgRg/n/q1Kl/23gUCoUClDmsCoXiJc4lGlkfbUQlgIs1qMWioCPh5mW2zxxCu693k1whE7WlDmdLDdo8HeubdiL9dhyOVSuQ+yAR69JeuFQPIOtaFBUHd+favG9AEMi4GIlnyxbk3rmLg6szlWv5kxZxhQvhEZQuXZqaNWsSExNDkyZN2LhxI1n5ekyCCmf/5hgexZIXH4OL3ztkxl7BUJCH5wefkbx3AbXG7SAvPYnr84ORJQMOZfwx5GYiGfXYlfZD4+xF/P5v0VrZYIGe3JwcnJ2dqVWrFp07dyYqKopr166xfv16GjVqhI+PD1euXMHV1ZXVq1dTqlQpBEF4JqP6Z8myzAVTNieMGVihwkoSOZ8skWOAgphrbGn3LpU7fkS92VPR6kXODB1LtQbtuPDjBloPmYYuP4fze0JIvHOdoC93UzbzIjOGdOKrr75i06ZNaDQa2rdvT5MmTQgJCeHw4cMkp6fhVLsa70wfQ669JVaIqJ4bR9jA8URt3s3otKvk6Ar56T+fYsrM4ft9PxC9ahuTJ0+ma9eueHp6sm7dOipUqMCoUaOoU6cOjRs35vDhw0DRvGZHR8e/dIwUCsUfEx4ezpIlS2jXrh22trYsXryY6tWrM3r0aPLz86lZsyZhYWHUqlULgAkTJvDw4UNWrVrFyZMnzRe5hg0bZq40USgUijflZXNYlYBVoVD8wqVkI6uuGXCzFrBQPxvQZCbHs+7z7tRcPwe1qwMmnUgxS5FiViKXV23l/MpNFGZkImi0VJs9jgv9x2FbrhS+g/sS8/16cu7GkZ+UTOmObbm/Yy/GvHwcS3tjodViZZKx1GiRZRlJkrgefYPqbfoQtT8EmxLlqD1iFbbuZUEUyb53jWshn5ObEIPTWx3IyUxDzkvDJ6gXurR4bm6bQ/X+84neOA1Ra4GpMB87bx+y4q4jaiwJeDcY+dYRrkdeJS0tDYCCggLs7Oy4ceMGJ06coFq1atSsWfMXnXJfhUhjDgeNaRRHi1oQuJ8jcf2RhINF0f2b3g3CxtWV0k2a4dWqKbHbd2GRYsDJxZu0+LuU8qvLxi96Y+vsQoPgkei1TpxfPppmTZvy9ttv8/HHHxMbG4uHhwdarZaTZ85wvqw9BQ7WJMl6bF7ShViWZWbZ+9D90EYy78UT1n8s3vVr0/bzEZSISmT5vPl89913BAQE4OLigru7O9euXWPJkiV4enrSpUsXNBqNklVVKN6AJ+fKJ0tP5eXlERISwvHjxxEEgb59+7Jo0SLKli3LwIED2bRpE3fu3GHVqlUAZGVl4enpSXh4OFWrVqWwsBBLS8u/c0gKheJ/mNJ0SaFQ/C75BpmN0UZcXhCsAqjUGqz9ypJ46gxakxqtIJNeIKGXoHij2siCjHuT+ph0elL2HeWtrQvJj0+keJ2auDd8G5NOh6hWc3d9KKZCHWpra9BYkRxzhwJBJisrC5PJhMFg4K13WvEgLhaPt96larfJOJWrgcbGAY2VHfalffGo/S4mg56ch7dQWTuge3CNy0sH4VyxNlbFvbgTtoy8pLvkP0qgID2RjFvnsXQojrEwl3O7VpJdKJGTk0OfPn3o06cPAQEB1KpVi4sXL9K7d28CAgIQRRGTyfRqj7Fs4qgxg2JoUD/O2N7NlLDW/PyY+uM/w5Cfj1O5cpz8ZDw25byxKueNZ6XqpMbFAPDR599Spnp9Diz9gqs7v2HC8h9IT09HrVabu37GxMQgiiKedWsgOdiRIhuw/pUlcwRBoHxQY0KadObktEUUq1iWkq0bsTZ4KHnFbbG0tGT48OF88MEHuLi4EBgYiJ2dHePHj6dnz57mpXwUCsXr9WQdZJVKRWFhIZmZmdjY2BAQEMCtW7eoWLEijRs3ZubMmdjb23Ps2DG6du3K1atXuX//PgAODg4sW7YMFxcXZFlWglWFQvGPpASsCoXiGVdTTeglsHpBsAqgcnVEU9adRyciMOYXFHWPVYkkJTwi+V4cpdo0xWg0Ufztmtzf9iOFD5JxruNPypXLlOv8ASWaNMS2lDfIMpWH9uH9A7spEfgOHn7VyExOxSCZKFGiBAaDgRvXrlDcrzF1x27Cs957z+yHxsqOiu1H4OTfHMHaCd29C2hL+CDaOHNz+xzKtx1MiXrvYeNehnqfbuS9Lem0XB5N4JS9NPnqNFaelSm0cqFbcDDXrl0jNDSUYcOGER4ezkcffQT83PH3SfbiVbllykdCRiMUnYLTdVBoAs1TZ2Svt+uTefcObtVrUKxSRU6O+5wsYzqeVQNwLePDnQvHqNGyEx+OX0rDbsMpWbk6FxIKOHToEKGhoTRs2JBDhw7h5+eHLMucN2ajx4QMiL8RULZb8xUAtQb14D8RuwkY1BO7kiW4eucWYz6bgFarRaVS8fnnn7Nx48bXkoFWKBS/tHTpUsaMGUNKSgqiKJKRkcHIkSOpWbMmM2bMYOXKlQQEBNC2bVtSUlIwmUxUqlSJ/Px8kpOTKVu2LBUrVmTx4sXmbQYHB+Pq6qpcaFIoFP9YStMlhULxjMP3Teay1BdJdciiZIfWRE1bzJlh46k67L9kxdzhxsr1uAT4UHPKSApEC/TJyeTee8DlwRNRWVthV7400TOX4VytCk02ryDxyAlSz13k1v4fMWam4T9qPDHfzKNJlw4sG/wJR6OT+fyKA27WL98XGRnRoyqFh7+jxJCtIMCj3TOxrVQbWZKo+P5wKr4/vOixsoyo0iJaqMlLvI0u9T651j6Mm/sd9vpU3N3dzY+TZRlRfHkW8q+QZZkLxmxs+TkIfpgroXrupQRBoN6n4wmfN4fAzyfiXLESquL2FFjr8QkMIvluNJLJhFpriSybcHT1oHjleiwJCWVgt/ef2VYOJpJkHRmyEe3v2EettRUeAX5ErttBwMDuiIV6nP0qY1/ai3qd29O7W/ArOBIKheL3WrNmDXPmzKFKlSo0a9aM+Ph47O3tCQ0Nxc3NjYiICNavX8+sWbN47733CAoKYuHChYSGhtKxY0c0Gg06nQ6A+fPn4+Tk9DePSKFQKH4/JWBVKBRmRkkmJV/G0/blj8m30GFt70atKWO5+X0IUUtWYMjOocqnQygVVBvJJIARLD1cqbtxIZeHTyFp73GSfzpF9THD8AisR3bMHbLv3OP2hu241K6BrX0xwr+cQrX6NfFo+jaiKJKldQGpEGQBXhI4SjKIdsWxKFeHzAML0SfexLpuV1yC+lPiqajbpNNRmJ1C0rkfid33HXnJsVi7lcGr1RCy9VDxcbAqSdJrC1SfMCKTjRFX4efQsdAI6hckKSu9357Vs76k7iefUvmDD9GpDehzjJSr2RC30pU5uu4rIg/vxM7ZlTbDZ2EQReo2a/uL7RTKEsgyOiSsf2dhzfshC/jGpwln5y3n9t4jqB3tKRX4Fnnyqy2PVigUvy43N5cjR47w/fffU7du3Wfu+/DDD8nIyKBHjx7k5eVRsmRJpk2bxvz583F1dWXs2LGEhYURGRnJt99+C2C+OKdQKBT/XygBq0KhMDNKRUvX/FrAJglFZbJaB3uqjRqMIS8PjY0NBYIRWZbMj5NlGY2jHQGrZmPMyUNtb4uNTosgC1h5uGHj6UHFXl2J2xlGpaHtqD8vmAalLShQGwEwSIAoIogv3xdZBkFjiSk7BSu/INz7LMcgakD1OFMqSYgqFSoLC7Q2DhRmJGHnVZGaQ5dhU6YWBqlozE+8idJWicfLAz3FJP/yNgBRrca3W3cur/iegMFDih5L0Q5rrWzQWFjRYdxivCrXACAhR8b4gj56JmRkfvt3+zSHkp6UalSXe0fP0Hz+ROx8K2AjaDE89TtWKBSvX1JSEmfOnGHJkiUAGAwGNBoNsizj4ODAunXr8PHxYfr06ezYsYPRo0czZcoUWrdujYuLC/Xr16dRo0Z/8ygUCoXiz1MCVoVCYWahKkpmmiQZ1UsCRbVJ9Ux0pbGxefyTUJQIfXxfUWBUtPyL2t4GyWgEirKeWns7KvTohCSZqDlzHE45tuTqBSSTAQ0ygqWA/e+oXRUFcKjbGau3upozlLLRhEoQEQQR4fHc0+SLB0g4vYPy732MvbcPAHk6IxqViMWrnZ76mzSPD5Aky+a5pFoV5Bte/PiAocMQH49DFkAjF/2stbKhQechzzxWEHjheDQIRcEqwjOv+1s6h602B7j5sgkEsBL+GfNVn2TDn/yclZXF9evXCQwMfCXLDikU/xSxsbG0bNmSjIwMbGxs0GiKurM9eY8fO3aMtm3bIssyN2/eRJZl9uzZQ5cuXWjevPnfuesKhULxSvwzvnkoFIpX5q90tBUEgZpuKtIKXv4Y5zxbZEF+nLN7/JoyaBER+LlxkCA/GzBoRA3ic7fJKgGNUUVhoQlHLegtBKpbFM2tqmiZjVoEg+nlGT1RABtt0Qsa9QZMJiOCqMLRsui2nPibXFszgQfHNlEmqB/23j7IkoQsyxhRUcxKpKT9mz0NioJAedGaLIzm2xwtHmeUX/T4x8GqJEsIgLX+xROMZVnGJMu4WP0yULMX1KgFEXtU6Pj9GdInX4hNsoyIgCUqnMXfMwv29Xs6Gy6KIidOnGDo0KHA788iKxT/BLIsv/C8/aTpm4eHB/fv3ycuLs58n9FoJD8/n+zsbIKCgjhy5Aienp7k5eVx4sQJunTp8sb2X6FQKF43JWBVKP4lJKkoEHm6o+2fWWe5vqcKnfTy5zrn2qE2qZDEnwMfowTOWhELRGSK5mNKMoiygERRvavG9GxBh4yMJJuw1GsxiipKOwroCgvZNf0r6tSpw9ypn1Gx4Drpul8LPgScLQVUIggaDZKgxlrKI/3Cj0VLNBQrQV5SLEZdAU7laxY9QxQxyUVBdj1PFcVeEOC9bv5qOwxPBfweNkXH7dd+WyaVjKVei9b44sKYTB2UdRBxt/nleLSCSDWVLVai+g+Eqz/TIeEkaHAU1HgKv9KR6zV6/gt9dnY2AwcOJDs7G4BSpUoRGBhIenr637F7CsWf9mRpmoKCoi7fT98O4OvrS+nSpfnxxx+5ceMGAGq1msjISBYuXEivXr2YOXMmp06dYurUqXh6ev4t41AoFIrXRQlYFYp/iScZp4iICJo1a0ZiYuKfyjSVthco5yiQkv/i+1WIuGU6YBSLsqxGqShAtdUI2AtqJGQs1Y+DL+lxKaokoDY9e7oxCRJaWYO+UIWdFoyqQq5/tQpLRE6ePAlAyr5F6BKiyNX/MsySHwfoNhqwVIPRJJO+ZyYP5rQi5eoRzi/oQ37yPbzf6YKlkzu5iXeLnifL5BrA1UqgaUnNL7b7JpQQLHAVtGTKRXXAVmoBN2uBgpeUBcvImFQSxXLtHhf3/lKuQaZxSfVLf+dVVXZYAlaI6P7APFTT4wsXloJILbXD7y4nflVSUlKAny/EHDp0iFu3bmFvb48sy0yePJmTJ08iSRIpKSk4OzubL978FdOnT2fcuHEcOXIE+HMXfxSKF5EeV3k8UVBQwCeffEL9+vVZs2YNn3/+uTkw1ev1APTv3x+tVsuHH35IaGgovXr1olevXri4uADg6upKmTJl3vxgFAqF4g1QAlaF4v+p5zNOOTk5fPHFF0ycOJHBgwfj4eHxp764C4JAr6panCwFEvPkF35RL5HljEuWHYUqE0bRhIetgCiAhSDiLGhAkLHUSJgEGZVRhZVegyTKmCRjUZArSqhkFdlXHnJt2QIs0m/ib+XI/FHj6NOnD/369ePkyZNYoqdO7gkKTAIZhUX7Ij8et/BUSai9BuSCTKwtNNT/8gilm/YgLfoMD8N3U8ynHsaCbLLvX8doNJGlAxs1dPJRU8Hp7ykdFQWBNloXtIjmoLWMg4hRljE9d7glZAo1Rorl2GGje3F2M71AppilgI/zy0/pxUQNFVQ2FBO1yID+dwStJlmmAAk3QYujoKaC6lfWGHqFjEYjEydOxM/Pj0GDBrF//35Onz5Ny5YtmTdvHkuXLmXUqFEsW7aMt99+m08/ObbXlAAAIABJREFU/RRbW1uuXLlCZmbmn2qe9eR9bjAYGDNmDOfPn8fPz49evXpx4sSJ1x6wKpnh/x1POpHfvXuX1atXc+rUKapWrcrFixf56KOP2LNnD2vXrgUwz1f18fFh4sSJTJ48mbNnz1KxYkUuXrzIgAEDlHWQFQrFv57SdEmh+H9KpVIhyzIHDx6kSpUqeHl5IYoiN2/epH379sCf73prbyEwtKaWjdEGrqdJqAUZR4uiTKpJhiwdqLNd8fbSInlmkYuRgsclwAAaRCRRwhkRMc+SfB2YLEyYLCQQTBhTcsm/Fsetb7/BxdGO6zNiaTd0OE6NGrF08WKqVq3K6tWrmTJlClevnmZw0xbszCxLXI6MShDRihI5sVeI27sUu8oNqP5udxrLd1h0ai3nYyMw5GZQvvMkSjXqBALYlPIn4fJRZA9fSpYqw0eV1LxfXvO3znW0F9R8ZOHObn0qKbIerVagsrNIVJqMvRZkUcKgNiHIAm5ZDjjl27wwu5qpKwpy/+uvRfP8Yq7Paa4pTpZsAmSSJD35sgktIurnjoMky+goyqB7CFrsBBXvaV2xFl5/hypZlklMTOTChQvs2LGDw4cPs2HDBlQqFWPHjqVx48aMGDGCdevWMX78eDp06EBkZCSTJ0/GxsaGwsLCP/ya2dnZ2NvbA0UBwtmzZ1m6dCm+vr6kp6ezceNGHB0dqVat2qseLgAhISFERkYycuRIZcmRfyGTyfTMVI24uDjCwsI4ffo07777LnXq1CEgIIBevXpx69Ytunfvzq5du7h16xYVKlRAkiQEQUAQBDp06ECHDh3+xtEoFArFm6dcllMo/h+QZdmcLTWZTMiyTEhICP7+/mzfvp0BAwZw4cIFOnbsiI+Pj7mk9q+URtppBf7rr2V8HS2NvIvaB+fowSQJ+LuqGBlgwfyK7syzrkg/Cy/8RTvKqKyopLLmPY0LX1tVYpZtRfq6FaO9qwP1RWfKRKQT3X4Yt/uOxrguhBVz5xCxbRd1q/px7OhRdDodZ8+eNZfBZWVlkZubiyo5mo1trVjazJIA9QMerhlK7snVVCpXBu8H+2h473smdg7E3cGSFu8E0nHBT5Rs1InMjFSSb0dSvM572NvZElTBlon1LfiwkualXZDfJHtBTRetOx9q3SgpWmJtZ8S7uJEM9BRKMi5Z9pRLccM53/YXwapBknmYKyPLMKSGFneb3z6dWwgi7bWulBWtcRcscBG0mJDJl03kPf6XL5soRMZRUFNCtMBZ1NLRwgM38fXNXY2MjOTEiRNkZ2cjCAL79u0jLi6OHj16YDAY6NKlC9u3b2fBggXUrl0bgKtXr1KsWDEAxo8fT3BwMHfv3sXW9lcWEX7OgwcP6NmzJw0aNGDhwoVcvXoVgOrVq3P3blEJeY8ePZAkiUuXLpnfl6+CJEnmz2eJEiXQ6/VcuHDhlW1f8c/xJFiNj48HipapmTt3LqVKlaJLly7Y2dlx7do17OzsOHXqFP/5z3/Iycnhu+++AzAHqwqFQvG/SsmwKhT/YLJcVAb7pIQsPT0dZ2dnAGJiYtixYwelSpXC29ub7du3M2TIEBo1asSmTZsIDAx8JaVibjYi7cqLtCv/4vs1gkBt0YHaGgfzbU9nFFxFLdWNBSzZsIToI0eYOnwQzZo1o3z58iRERiPUqE1gYCA//PAD58+fZ8KECQwZMoTNmzdTo0YNFi5cSKVKlcjOzqaGmz2aKkYW7V9DWFgYrVq1Ytu2bRw/fpzo6GhGjxzOmjVrmNS2AQcuXmTnpjU0ad+dbl2HUL7HLNxt/nlf/ERBwEuwxEtriUmW0VtI3BVlDt8zcSdTRi+AvYWMWihaw1Vvghw9qAV4u4SKxiXVf6hxlPXjbOktUz4XTNlYS0Wdg39eqRW0soiNqKKmyo6qaltshdfzp8JoNDJ58mS+//57GjVqRG5uLnv27OH69evcuXOH2NhY3NzckCQJV1dXLly4YP7SD7Bnzx5at26NVqslKCiIwMBALl++TGBg4O96/W3btqFSqfjhhx/YsmULI0aM4KeffsLKyooHDx6YM6/Vq1fn0qVLfPTRR395zE+yZU9/NuvXr8+pU6e4dOkSgYGBODg4/MoWFP90Ty+5BLBhwwZWrFiBs7MzDRo0YNCgQbRt2xatVotOp8PCwoL79+9z/vx50tLS2Lx5M7Vq1UKn06HX69Fq/xmduRUKheLvopo0adLfvQ+/afny5ZP++9///t27oVC8cU+urMfExHDo0CG6detG+fLlsbOzY/369YSEhLBy5UoGDhzI+PHjsbOzw9LSko0bN1KlSpW/rVvkky9r69evx9LSEnd3d+Lj4/npp5/44IMPqFixIk5OTixatIgBAwbg5eXFuXPniI6OpkuXLjRq1IgOHTrQv39/wsLCCA4O5tSpU3h5eVGrVi2io6NJSkqiVatW2NvbExUVRUJCAkOGDEEQBHZu30Z+ehKzpk+hf7cOlLIXsdP+84LV54mCgEYQcbUSqeOhprqriFosKsHWmYrud7IUaVFaRRcfDf6uKqw1f3xMKkHARdTip7KljMoaB0FDMUGLm6ilrMqatzQONNE4U1JlhfY1rLv6ZJ3U5ORkFi5cyJYtWzh58iSxsbE8evSI9PR0UlNTuXPnDkajkbi4OKpXr87u3btxcnIiKSmJTz/9lIiICN5++22cnJwIDQ3l4MGDDB8+HCsrqxe+7pkzZ8jMzMTNzY3CwkK2bt1Khw4dqFWrFvXr1+frr7+mWLFiBAQEcPDgQTw8PPD29sbNzY1PP/2UESNG/OWLQE8+09HR0SxZsgQnJye8vLyQZZmIiAjs7e0pW7bsn97+1atXsba2xsLi7+nm/P+FUZKJSZc4n2xiX6yJH24b2HfPyOE4IxdTTCTmyhSaiipNtL9Rav+8J+eZuLg48vPz2bBhA7NmzaJGjRpMmDCBEiVKUKdOHbZs2ULDhg3N5eZ3795lypQpZGRkMH36dDp37vxMKbFCoVD8202ePDlx0qRJy5+/XcmwKhT/IM/PdQJYuHAhixcvpn///nh4eLB+/XpatWpFZmYmbdq04dNPPwWKmi7dvn2bKlWq0KZNmzfW1fRJWePTX+QPHjzI+PHjcXd3Z8+ePXh4ePDll19y/Phx7ty5Q2BgIP369WPGjBkcOHCAFi1aULt2bXJzcwGoWrUq165dY8SIESQlJbF37162bt3KqFGjOHPmDKNGjTKXzXl5eVGpUiX27dvHuXPn6N27N127dv1XfGF3txF5r/zrm7khCAIeggUer7Hc92kbNmzg6NGjdOvWjcDAQFxdXUlOTua9995j1KhRdO7cmUOHDlG7dm2io6MJDQ3F0dGRlStX0rdvX3r27El6ejphYWG0bduWpy9kFhQUMHnyZHMFAvwcGK9du5ZVq1YhSRJWVlb069ePDh068OjRI65cuULz5s0BmDBhApMmTeLKlSvs37+fVatW4e/vj0ajoU6dOmRmZprLkH+v57Nt2dnZTJ48mRMnTtC2bVtWrVpFrVq16NKlC4cOHeLChQvUr1//pUH3y6xZs4YVK1ZgYWHB/PnzqVq16j/+As3fId8gc+ahiaP3jeQYHq/lrCn6Z/+4iiHfIBOeaOJkgglRgNruKt7xVuFh+8vP4tPzS6HofXjkyBG++eYbunTpgl6vJzExkQ0bNrB161bat29PixYtsLe3x8rKioMHD9KzZ0+0Wi1Tp05lzJgxODo6vuGjolAoFP9syhxWheIf4EnH3yfBanJyMgB5eXlERESwZ88eRo8ezdKlS4mOjubGjRt06tSJ6OhovvvuO7Zs2UKTJk1Yu3YtlpaWjBgxgrfeeuuN7LsoioiiSHJyMhEREQBcuXKFjz76iN27dzNv3jy2bNlCamoqNWvWJCoqiujoaAC6dOnCzJkzAWjXrh1du3blhx9+YMmSJZQsWZL79+9jYWGBt7c3I0eORJZlwsLCqF27Nn5+fsybNw+AevXq0bVrV3NTnH9DsPpvUVBQwPnz5wkMDGTjxo3UrFmT2bNnExwczPfff0/lypUpWbIkLVu2pFSpUgiCQGJiIseOHaN+/fqoVCqCgoI4dOgQZcuWZfz48ezevdscrBqNRgC6detGy5Ytza/79Lzvs2fPMmLECI4dO0ZQUBCbN28G4D//+Q+LFi0yP6d9+/aoVCrOnTvHxIkTcXR05IMPPsDPz4+6dev+oWD1yQWjJ8HqzZs3uXLlChYWFrRq1YqIiAj8/f0JCwsjNDSU1NRUGjdubG449UesW7eO9evXM336dA4ePIivr68SrD5HlmVupJmYEa5j9x0jWjV42QmUsBVwsCjKoqpEAY0oYKsVcLMR8LITcLWGC0kmZkXoORhnwPBcG+8n0zWSk5M5cuQI3377LW3atGHIkCF07dqVevXqcfz4cQoLCzl37hxffvkllpaWFBYW8uGHH3Lw4EF0Oh1QdP5XglWFQqH4JSVgVSj+AZ4Eqj/99BP16tWjY8eO/PTTT9jY2BATE8O1a9eAoqUNGjRowIIFC+jTpw/BwcFcvHiRLVu2MHv2bObPn2/+ovq6MqzPbzczM5PevXvTokULjh07BkB4eDgVKlQAihrKvPvuuyxatIj27duTmJjItm3bMBgMTJ8+nX379pGYmAgUZSssLS3ZvHkzlpaWtGnTBldXV27evAkUBbirVq0CigKU48ePA1CmTBmCgoKwtLR8LWNW/HE5OTkMHz6cYcOGYWNjw8KFC1m4cCEDBgygbt26xMXF4efnh6+vL7dv32bQoEHMmjWL/fv3s3HjRgDKli1LaGgo+/bto1WrVgwePNj8/ntykUetfrZQKDc3lylTpuDr60tYWBgAS5cupV27dgDExsZSrlw5CgoKaNq0Ka6urqxYscL8/MDAQNLS0hBFkVmzZjF37lwePHjA8OHD/9D4n3wO165dS48ePejXrx/bt29HFEWaNWvGkCFDWLlyJfPmzaNEiRLs3r2bwMBA9Hq9eV3ZX7Ny5Urz0ic3btwgMDCQBg0aYDAYMBhesqDv/yhJltl1x8jSywZEoShQtVL/voBeLQq42wq4WcOe2yYWnS8gR//zOTArK4tJkyYxduxYwsPDGTRoENbW1lhbFy0BVaxYMdq3b48sy6jVak6dOkW3bt3Yu3cvnTp1YsOGDdjZ2b2WcSsUCsW/hRKwKhRvmCzLL1xDtU2bNqxcuZJly5bRrVs3Nm7cyMWLFxk5ciRff/01UHQ138bGhuPHj3Pz5k2aNWvG3Llz2bZtG40bNwZ+LtF9lRmWgoICoChIeH67Bw8exMHBgQsXLvDJJ58AUK1aNXbu3GkOLlq1akVKSgrTpk3j4MGDHDp0iH79+pmPxZOmNqIoUr9+fUqVKsWmTZsICgoiPz/fnHEaOHAgZ8+e5dy5c7Rq1YrDhw+/sjEqfptJLuoonCsb0cnSCy+KpKWlMWTIEN566y3y8vK4cuUKhYWF3L17l4EDB9KjRw9mz56NhYUF69evp1evXrz77rs0atSIkJAQhg8fTk5ODjk5OXh6ezNv4xq+S46m1BcD+cb0kG908azRJXBJziVXNj7z2jdu3KB58+Y8evSItWvX0rhx42f2MTg4mEuXLpGamsqoUaN4+PAhCxYsYOPGjWzevJldu3Zx7tw5/P39gaLPW7Vq1X4zY/+izzQUZT5Xr17N6NGj8fX1JSoqiosXL5KUlMSVK1fYuXMn7dq14+bNm2zevJn8/Hx69+5N9+7dXzhX9vkgdv78+ciyTIUKFThz5gwjR46kX79+DBo0iE8++YTIyMhf3e//BZIssz3GyKE4I562RXNS/wy1CN72AkkFIksu6cnIL7ookJOTw6xZs6hevTpjx45Fq9XSo0cPFi5cCEDx4sUZO3YsV69epVWrVnz66ac0adLEvPTY8xdcFAqFQvFLwpua5/ZXBAQEyOfPn/+7d0OheKWys7OJjY2lQoUKWFtb061bN+Lj4zl27BgpKSl8//33SJLEmDFjqF27Nu+//z6JiYlotVoyMzOpV68egwcPNm/vRfNf/6obN24wffp0c4MagN27d1NQUEDDhg1xd3end+/e5jmq+fn5WFtbk5OTQ/v27alfvz7Ozs6sXr0ak8lE+fLlWb58OVZWVnh5ebFp0yZatmzJkCFD0Ov1LF++HKPRSFhYGMuXLycsLIyFCxcSGxvLxx9/TNmyZYmOjsbHx+eVjlPxcrIs80g2EGnKJdqYhoABkJEQsBNsqal2ooLKmjtRN6hatSoLFy7k9u3bDB06lPPnz7No0SLatGnDZ599xpQpU9i6dSuDBg3i0qVL7N69m4SEBCIiIujUqRMeHh5cunSJDz/6iCHfL+SynIdOlrBEwFICtUqNDBiQyKEoQLSIiSd+zxHaNm1OQUEB27ZtY/HixeZ9FwTB/H9eXh42Njbk5eUxdepUvLy8GDJkCEePHmXdunXExMQwfPhwPvjgA/Nz8mUThbKEDFgIAjaoXnoxKDs7m2PHjlG9enW8vb357LPPMBgMzJo1i8TERBYvXoyLiwvDhw/Hzc2NL774gvj4eBJTUqjYsC7V27VAtLHBUhBxE4oaYFkIInq9ni1btiBJEj169DC/Xt26dfniiy9o3bo1M2bMIDIykrfffpuUlBQyMjJISEggNDT0Nb9D/tkO3zey45YBbzsB8Q9exEuOvcH9a+FUafAuNo7FAbhxej/7V82lSsDbTOjZhoYNAhk4cCCPHj0ynyMzMjIoX748UVFRuLm5mbf3ZF1VhUKhULyYIAgXZFkOeP525dKeQvEamUwmwsLCzJ0goejLzOTJk9m3bx9NmjQhNTWVrVu3Mm7cODp27Ehqaiqurq74+Phw+PBhbt++zaFDh9i9ezcuLi707NmTsWPHmtejfOJVBqtnzpxh9uzZnDlzhuHDhzN69GiuXLnC8OHDsbW1JSAggFWrVvHDDz/QqHFjTl6+wL78JAxqEbU+DxcrLYtWLKdvl2Du3LnD2rVrOXv2LPb29giCgI2NDcOHD+fKlSu0bNmS8ePHExgYyIMHD/D29sbW1pYHDx4QFhZGUFAQ+/fvx97eHkAJVt+gdMnAAX0KaXIadtIj3MhHfGo9WJ0kMWz4Ws5s3IeLmxs9O3dl544dTJw4kY4dO9K4cWOaNm3K3Llz6dSpE5999hmVKlXiwIEDXLp0CZPJxIEDBwgICODDDz8kMjKSb1atwKJDU87K2TihwV54HCA+lXBUoSLlWgybP/+SzLR0KjVrgO3Vi+Tdf4goioSEhJCYmIiVlRW5ubkMHDgQR0dHbGxsALCxsSEnJ8ecfX3nnXeoU6eOudGRSZZJkAq5ZMrmnlRoHrEMuEoqsvafpsM7zXBxKmrwpNfr+fbbb1m8eDEtWrQgNDSUPn36ULp0aZKTk8nMzMTDwwOtVktYWBg9evRg7969LFuzilRZT50vh2PlYEcsAqJUgITMJVlCaxSpprLFX2NHbGwsBQUF5otCDx8+JC8vj7lz59K6dWvGjRsHgMFgQKPRcPHiRebNm0d8fDxeXl6v943yD/UwV2L3bSMeNn8sWE28fY29Sz4nMyUBn/oteRhzlTLV6xO+cyU3Tu+jw6jZXLt6heDefYm7Fc3IkSP54IMPuH//PiVLlsTJyYl69eoxevRoQkJCzNtVglWFQqH4c5RlbRSK1+BJBmfcuHGcPHmSc+fOAUXB1p07d0hMTCQkJIQSJUowcuRI/P39adiwIZcuXSIyMpKmTZtiY2NDeHg4WVlZNG7cGB8fH3Q6HUOHDiU3N5du3bq98rlPkiTRp08fQkJCKF26NLVq1SI4OBhXV1cyMzMJCAhgzJgxXLx4kRUrVmDlU5Z77jaEnzpF9I0bFA+oxvppcwnbswdt+3e4ezICG1FNDX9/WrZsyaZNm6hevTomk4kFCxbQvXt3XF1dcXJy4tKlS5w7d46qVaty6NAhADQaDW3btqVOnTrmOWGKNyNJ0rFddx+NdAsnOR1tUY4RBA0IGq6duM60tuOJu3SD1gNa8+704dy8eoeoU+GcOHGCrKwsihUrxr1798jJyaGwsJCWLVvi6+trnqes0WgYN24c7u7utGjRgg+7dyOqkgtZJj0uogUq4eVLEe2euwSPCuUYvGIRlRrWw+hfjg8CG5MZl8CUKVOws7NDr9cTGhpKYmIiJUuWpHjx4iQkJPDNN99w+PBhevXqRcmSJYGi9xpApmRguyGZi6YcCmQTjmiwEdSI+Tr2f7mQ9Z9/yclTJwmLOI0gQ40qVcnIyODHH38kNDSUypUrM3bsWCpXroyDgwMxMTHk5eXh5+dHdHQ08fHxWFtb4/lOXXKa1qB884a4WNpigwpLRCwFFbHHw7FTaXBwcOCOPpcoOZ/K1o7cjrzO+fPnWbZsGcuXL6dXr1789NNP+Pn5Ubp06WfW7Jw2bRo2NjZ06tTpjbxf/mlkWeb7SAM60+8rAzYZDRxYPhXX0pU4tXkprqUr0W3qGirUboKjmzdqjRaXkhXwb96R60d3Ebl3LY+SEnAs5kqrFk2Jjo4mPDycoKAgAJo1a4a/v/8zGVaFQqFQ/LqXLWujlAQrFK9IoZRHuimJQjmHqCsxtKvTGZ2pEI2gZc6cOdy/f5/Zs2djZWVFamoqI0aMICMjgxIlSnDz5k2OHz/O2bNn6dy5M9evX8fGxobY2FjKlCkDQHx8PBs3bqRatWrmL0WvgizLJOTKRKWZyNZBdkIMQW/54GEjMGHCBIoXL87QoUPRaDRcv36dAQMG0KRZM+5pTJw/dZpB3y8k8148B75ZSdzV61SoG0CbEYOQgTVjJ1Gvx0fcXbWd3dtDmThxIqdOnSI5ORlPT09EUcTR0ZGtW7eSmprK119/zebNm2ndujWff/75M0uUKN6cdMnAVt19HKRbaJBB+OUczpNbDrNk4BzmnV1OifKeyFIet+KN/PDJd+TFJeDu7k6PHj1QqVSEhIRw/PhxoqKiiI+PZ9u2bYSGhuLt7c3+/fuxsbFBkmW26ZJIkvUUF7W/un/3LkeycuhYJuzfisXjCxmFson79+7xkX0pqpTwNpf/xsXFMWjQIKZOnQpA3759qVevHiNHjqRcuXLPbDdDMrBFn4QJGUdB8+xrXrnGZ3Vbsq4gAYDt8xbz6EECq+YtwPAgmWHDhpGWloa9vT29e/cmODiY/Px8Dhw4wMyZM1GpVHh5eSFJEuXeqonbkC44CRrUMs8siwJwfvc+Di5bzbiwTZiMRgpVAgWykdRFG9i+Zh0DBgzg448/BmDOnDmEh4ezbds2zp07x7JlywgPD6dRo0aMHz/+b1uL+e92N1Ni4UU9Xra/fz7/ugndUWu0pMTF8N8le7G0tkUymRCfql6J2LWaqBM/0mvOFn7asYHrW2YTdyuKc+fO0adPH86dO4eVlZW5pFyhUCgUv59SEqxQvCYm2Ui88SbppmQEBFSoKVXNg5LlPTl4cxu1ytfH29ubR48emUsOb968SWFhobmLqUaj4YcffuC9996jc+fOPHz4kAoVKpiDVQAvLy9zU6NXJb1QZt11PXcyZUQRNCIYKMeVCB0VnVSUquDDzchLXL58mdq1a3Pjxg08PDyoO34QmpQE1k+cwfV9Rwjs2oF+336FvqAQS5vHAURuHs7Fi+NS0otLjlbsP3GMzp07ExMTw+TJk3nnnXdISUmhd+/eBAUFsWjRIqZMmcKYMWOwtbV9peNU/DGHDY+wle6+NFgFqPN+A77+7yxy0rI5fuEmuxZuoU7bOtTq24a1Pcbj6+uLl5cXjRs3pnTp0jRs2JAffvgBWZZxcnJi9OjRREVFmct0E2UdD9Hj8lyg+CJ2xZxRa7Xo8wuwsLYmKzmVbVPmcD7sAMca1OfIllDzdjMzMzGZTJQqVYpixYpx7NixF1Ym6GWJnfoUpBcEqwCl/X1xL1+GlNj7uJYpSYmS3uQ+Sme/mM1bkgFra2uaN2/OqFGjgKLmU3Fxcbz//vtUqlQJg8GAn58fAz4eSryjhkqCBq0gwgtimlptWrLq43FkJqXg6O6KpdGIrFKhC6hM88QWBAT8/Ld8yJAhzJ8/nwsXLlC+fHneeecdZs2aRfHixX/zOP6bnUwwYaF6ebAqyzKyJD0TjL4TPIKNk/6DlY09Rl0BWNs+cz9AbnoKbmUqA+BoreHB3Zvs2buftq2DCA8PN5/jlWBVoVAoXh2lS7BC8RdIssQ9w3UyTMlYYYuVYItWsEQrWDJh7miWTlzNQ+Mtdu7dRnx8PBkZGUDROqsajYbLly+zZs0afH19iY2NBWDmzJlvZK5Tpk7m64s64nNlvOzA01bA1VrA007A0wbuZUnEONbBIAs8qXCwtbUlLSebNStWcnLGYmq825ziJYvmx4miaA5WAa4dPo5zCXcq+VbFrXJ5BvTvz9y5c3F2diYmJoaUlBRcXV1ZuXIln3zyCeXKlUOWZSVY/ZulSXpSpEdYyIUvDVYBjDoD1d6pwYSmHxN18io9Zwyg47ie1G9egeJlSiLLMuvWraNdu3a0b9+exo0b06pVK4YNG8aQIUNYsmQJlStXNm/vijEHDS8vAX6aWqvF27cy1w6fKLpBEPBr0ZgZ53/iavg5QjZt5NChQ7Rp04YePXrQqVMnihUrhizLLy2jv2vKJwsjDr8SMAfPncKWiUXrBl89cISchCQyMzMxlC8KzE+fPs3WrVtZvHgxjRo14siRI0DR3MVDhw5Rs2ZNHmSl49umBRbCy//8CoJA52kT2DZlDlAUXNkKaorXqILsaMuRI0fIzMwEwMrKin79+pGSkoKTkxPdu3f/nw9W9SaZyykmiv3KKleCICCqVBh0hWQ/SgLAy6cmxb3LISNz+/xRJElCMpmQHneAzs1IxcreiazUhyzp15hbZ/fTavxqytVuAmC+SKJQKBSKV0spCVYo/oJMYwqxhkisBLtffNGWZZm6JZpT3L0YlWuUR85WY9DM/6AtAAAgAElEQVQZ6du3Ly1atGDNmjUsXLiQVq1aMWbMGEqVKmV+riRJL1zW4lXadMNARKKJErYvDxAScmUKT6/BLusO/fv3p2zZsnyyaim7122kcdeONOz+0Uufe/N0BPuXrMBkNHLvUiSipZZl8xfi5eLGl19+ybRp06haterrGJriLziqe0SsIRIHoRD5VwJWgD1LthMyYTnLb23GwaWoqVjirdusmbGLvu92ZPHsuWRmZtK3b18mTpwI/Ny5Nycnxxw85shGVuoSKIbmdzXHkWWZHxcsI+l2LMFzJpnLggGWDBlN84B62OXqsLW1pX///r9re+v1ieTLJmyFlxceybJM/xJVcXR3pUwNP/IyM9Hr9LzVvSPf9hjEoX372bNnD5mZmYwZM8a8PA7AgQMH8KtZgx22BVijQvMrASuAyWhkoLcfX985bx5frmwk9tBJ0ncfpW3btrRp0+Y3x/a/KD5H4qvz+mfObZnJ8Ti6/dx8qiAnk0MrZxJ9IoxK9VpgaWtPy/4TuXnmIFum9ccnsBXtRsxBa1l07PMy04gJP0Slus1JS4glJe4mtVp1JSFHpm05NU1KKQVrCsX/sXfe8TWe7x9/P88Z2VOmSJCYCSIEEUTNhlJ7z9YebSmqrV/NojWqtFpUrVIjKELNWBEy7ISIGYJEFrJOzsk55/n9EUltodpqv8/byyuvJPe517nPk/tzX9d9XTIyf5ZnuQTLglVG5hWRJIlLuhNoJQ1q4elH+Ts27CEs9DDTV32Knd6V8+HXGDZsGNOnT6dXr16kp6cXW0OKPot/hytZjk5iYoQWJ3NQis92mdMbC1MxSAfnU6d2LfoPG8JS7U3sUKF4QT8TT8eyYdLX+DRpSJP3e7H/t63cDYtkx9oQzpw5g5+f318xNJk/gSRJ/Jh/FSdjPLcupBJ7+DQ+DX3x8Cn/1PJ3kzOY1WMS2rx8puz6BqPByDd9JmMQVfy8dTsFpy/SuHHj4vLPOohJMOSyqyAdR+H5d1cfaft2CiGTv0aTk8u7Y0dy68IlwteEYOnswKA503jf1fuRcT2c3uZx0o06Vutu44j6hZ+/Yxu2cCJ0NyN/+ZECrZZzByNYOnI8X0yfxug+7xVH6S1q12g0FkfwvmbQsK0gtcTj3DHvRzRZ2XSe9AkAkRu3kZp1j3pY0aZ5Cznq7DM4ecfAL+cKcLMqfC/zsu5yYsdqyvnWx7l8Ve6mJHHvThI3zsXQpM8YEiL3snpCbz4JOYudiweLhr+NXpuPjVMZmvQbS1piAhEbF1G6ki9tP/wKlalZcVt38yXKWosM9i352pWRkZGReTqyYJWRec0YJD2x2sOYYvnMTa5er6exZxt+j1uHjbUNlU3qcuTIEWrVqlUc9dZoNAL85RbVh0nINLDo9B8buudxO1uiUvJO3g6siWVZN9bqknEowYb7cXFw8eoVyuUL9Pdr8Kf6LvP6MBqNXLt2DYVCQbly5SiQjCzKu8DByVM4H3GeRt2bE7Z8Bz0nD6BmizpPrSMzOYPhVXvi7l2OWxeTsHOxZ8CisbzdpD+NlHYIgvBCj4Gz+iz26zNxeoFF93Hy7mexe+HP3E64THbmXd4dOxKvoABEAfqZuHHs2DGWLVtG3bp16dGjxzPdzW8YNGzRpb4w2BMUWj5Hetbim3NHMbMqrC/ySAR96wbhb+UEPLgfKUlPjPm8IYc9BRk4lVCw6jQaRnrWpu3YERxauQ5Lezs6L5zByJqNKC0+x9/1f5zwm3rWn8ujnH3hejIaDOz47nPO7NuIrbM7wUMn4VGtLnlZdwn9djwF+Xnk52ZhYetAv1nrORu2md2Lp1CzZVdSExPIz80iqMeHVKzb9Im2cgskzJQCn9R9ubUrIyMjI/MkctAlGZnXjMSLLaJKpZLugzry648bGTL+PQAaNmz4SJm/U6gWoTdCiQ25gkSTVu9S3l7BHaO2xG0UzYtBr0ehVOJY3gMPhXzH602gyAqo0WiIi4sjLi6OwMBA0u/dJb9eaaxKWTHml4lkJKezftpyrpy6SPUmtVAon8z1a+9airYfduHyyQSm7f0Wr5qeaBDQS8biNfCiNa4HkISnBiB6HuY21nT4fDQ34uK5l3wH78aB6CQjBiSWLVtGSEgIY8aMYc2aNcTExDBz5kxKlSr1RD0GpBK3rVAqaT6oL3t+WEa78YWRej0b+KNS/iEgH4/6W0Th0VTJD4nVZmY0G9iHuLDDjFixkPK1apAu6R7UI/M09Ho9BiMoH1i5dZpc1GYW5NxNw9zanuBhk6ng/xYA5w6FYm5tR6eZa7h18QwL+gWSey+dGs06cuHobuq07YuZlR0m5s++Vy8Axjf/3F/mBRgMhteay1xGRub1IgtWGZlXRPHgn0EyoBCe/Ydu+OcD0Is6TIU3J4+ojYmAUXrSCvo4kiRhlASsH+QxVCO+xHa7EIWy8DFjQMIceUPwT3Pz5k3CwsLo168fFhYWnDhxgkWLFrF27VpWrV5NRPx5okMjCV9/GAd3J8aumYx3wxrPrbPnlAEPHVBo0IummD/nLujjmApiyQ9QHvDw2jWztGTTT7O5deESmampBA/uj4lez7BhwzAYDERHR9O8efNiV93HUb/gPunjdPh89BPRY02e8wwobgcB4SVVedepnz7xGVW9rLL/j3Px4kXs7e1xcHBAqVRiqjQQvngCCXtX4+rlQ2DX4fSYsoyIkEWcDdtMBf+3MOgLSL4Sh7WjK5qc+9yIi8bUwoaz+3+jfsdBdP1icYnaNkhgopDfj387RWI1ISEBDw+P4mjPMjIybwayYJWReUUEQcRBUYZUww0UPPsEXqFQoJMKsBdL/429ez5ulgIuFgLZOgnr53iy3deBh3VhWQAbQYmNoEQjGTArwQa9CEmSMCJRViFvAv4JHr4fbW1tzZ49ewgLC8PBwQErKytatmxJtWrVqFWzJpfyndhRMJV3RrSncc/CfL+JZ69wJzGZeu82fOohx8PfKxWgFWxwLoF7bRHWgvKlDkIedzFOu57EhSNR3Dx/kf6/LqS8gxObIyOJiIggMDCQdevWUb169WfW5yCoUTywCitLIF6LxKokFftZ4FwCN19HQY3Eiw+KHubhcgWSESUCdiVI/fO/gNFoRJIkhg0bRufOnRkyZAgJCQkM6d2PK4lJ+DbvTOKZo6yd2J/qhzMpVyOAhGN7SLlyHhcvb8pWD+Da6Qhmda5BnbZ9GL0mCltn95fqQ14B+DrKgvXfREZGBlZWVqjV6uLP4q+//sqMGTPw9fXF1taWmTNnYm1t/U93VUZG5gGyYJWR+RPYK0uTZkhCL+lQPmPDqpXyMBUssVbY/829ezaCINDaU8FPZ/SYKSVUT7EQ6AwSWVroWUX5h2unIFBbYU2YPgOzl7CW5mHEXlDj8hJBdWReD4+Lu3v37hEeHo5CoWDz5s34+fmxf/9+1q5dy7Fjx/Cv60vdXh04/vsRUq6moMvXcWp3NA26NqEeDZ8vtB6444qCAx5iyQ8nSgsmWPPig5CizaUoiuTn5hEZshXvoEAqBtSm86RxxO47jEPVitQxdybKzY02bdowefJkrKysOHfuHBs2bGDixIlPuP6ZCCLVFZacNuTg8BLZ3gRBIEvSU1Y0xU58sYi0E1V4iKakGnVYv8Kf3/voqa2wfmmL8H+NIqFa9D726tWLNWvW0L59eywtLUm8cpHSfi3oMG4eCcf2sn7qIDZ/9SEdP12Aa4Vq7Pt5Bm6Va2Jp70jLwV/QoOsw7Fw8XqkvBUYoZ/O//X78m8jKymLbtm2ULVuWpk2bcu3aNZydnTl27Bh79+7FwsKC0qVL4+Pjw8CBA1Gr5b9ZMjJvAvJTVkbmT2AimFFOVQM9BeRLuRilP26XGSQ9GikLJSo81TUQ3rBNZg1HJZ0qKUnJk7iTK2F4cBFLbyz8PjUPulZW4u3w6Oa+osIcUxTkSoYStWOUJLLRU1dh/bdEQJb5w6IKhfdHU1JS+Oyzz1i2bBmOjo6sXbuWRo0akZOTA4Cbm1vxps1DNMWvU2c6je8GSKhN1UzaMZtO43q9sF2jUUOWZIOfyu6FUaQfRhQE/JXW5PDkmioKSlYkVvOysjm5Yw9ft+nBidBdfN9vOBfCI2kxpD9mjnZEf/szToKagQMHIkkS3bp1o2/fvvTo0aM4au/T8FFYISGhl0p+Q9QoSWgwUktRckuMn8IaDQZeNuChQZIwAt6Kl8tTXCTugJdu803DaDQWH8AoFAquXLlCUFAQ48aNIzo6mk6dOpGRkUE1Hx/QZpN4OQEXL2/cffw5e2AzAE36jUOhUpGXdZdqTdqjNjV/ZbFaNJ9lShC8Tuaf4/bt20RFRQFgbW1Nbm4uAwYMoFatWkRHRxMVFUV2djajRo2iRYsWjBs3jgEDBshiVUbmDeLN2kHLyPwLsVbYU1ldB3uFKzryyJdyyJdyMKDHRVGBSib+qIU30xX2LQ8lH/ub4Ouk4E4e3MqWSMuDWs4KxviraVjmSSuQqaCgndqJfAwvFK0GSSJV0lFLYU1lOeDS38bDBwO///47PXr0wMPDg/PnzzNw4EA8PDyoW7cuP//8MwCVK1embt26HD58mLcaNiL++19RV61D9wld6TahLzZOdi9uVNKiF9UYFG64ZBW8tDiqqDBHiYBW+kOgwh8Bm4rGtOObHwmds5BWHw5mzKaVBHbrQMjU2UiSRIUWjblz7CSCIKBWqxk/fjzvv/8+N2/eZPXq1UydOvWZ91hLiSoaKG1JpwB9CfpufLC2ayos8XiJiL0eoimeojnplHyOjJJEuqSjlsKqRJbchxFFEUEQSExMJCsr66Ve+6awbds2Nm3ahCiKiKJIYmIiQ4YMISAggEuXLrFw4UKCg4M5deoUK1asoG3btmhSrpB4KR5Le2cMBTokg5GY7aswtbCi++RlvPPBdMwsbZ7bri4/j50/TGTDtCFcj4164vd3tVDBVsTJXN5KvckYDAYmTJjA2bNn+fnnn9mzZw/W1tYMGzaM7t27Y21tTXx8PLVr1yYqKqo4Z/Tt27f/4Z7LyMgUIT9lZWReA6aiBe6qylQzaURldV2qqOvhYxKIi6rsM12F3xTKWov09lYxq7EJ0xuZ8HVjE3pUVeFu/ezHg6toQhe1CxKFm/YcSf/I5rtAMpIu6cikgAClLUEP0pvI/DUYDI8eHCQkJLBv3z4A0tLSGDZsGM2bN+fEiRNkZ2ejVCoJCgoiPT2dmJgYLl68SOXKlRk9ejT9+/dn6cw5eJt6kYozoAFJB08RV0ajESQjgpSHDpEzp3IJ6TySwb378u2335Kfn1/iMZgJClooS3FXKqDgoQjDlyKPs3T4OI78ugl9QQGN+nTBws4G44MxB48cSOrVRI4fj6FLx46Ud3KlQoUKDB06lNWrV7NixQo6duxIxYoVXygQayusaaCwJQMdWY+t6SIkSSJXMpCKjhpKKxor7V9qbSsEgWCVA66CCekUYHxBn/QPhHFVpSUNlC8+OHi8z+fOnaNDhw507NiRuXPnsnr16qeWe9O4fv06W7duBeD06dNER0ezYMECgoODGT16NA0aNKBBgwZkZWUxePBgkpOTKVeuHK1bt6Zv376oMBK5fCoLBzdDZWLGe99spk6bviUKNFfExhnDuZ92G89ajdi9ZCrHt/9SXEaSJHJ08JaHHEjuTePx56EgCFy4cIFmzZphbW3NsmXL+Oyzzzhy5AgA/v7+eHt7k5WVRUJCAgcPHqRZs2aEhIT8E92XkZF5CvIdVhmZ14hCUKJ4ieiobxJKUUD5EkdYLqIJ/U3cuGbI47ghi3SpAOHBXk+FgL/CBm+FBbYvaRGSeXmK7vIVuUseO3aM6dOnc+nSJWJjYwkNDcXT05PRo0fz7rvvAmBnZ8e7775L586dqV69OnPnzqVx48Y0btwYgCaqUpgKCs4UmGItpWKGBiSRwnNOAQEJhaBHj0BCwn2MOJDw/XrGfTSKWrVqERAQgJ2dHT169MDE5MU5Kg0GA5WUFjTDyP6CDExzdOybv4T4Q0d5672e3Ig9x+0Ll+g69VPKeFcmI+kW91JSsXZ2JGBQL0JHTWJWzEmsBwygW7duNGzYkClTpnDp0iW2b99eonkUBIF6KltcRBOiDfe5ZcxHkASUD2L76il0yy0lqGikcKCSwgLxFQ5iTASR9monDuoziTfkggQ2KB+5m6qRDORgQAAClbbUVdqUqK0iMRYREYFGoyExMZH33nuPVq1aMXr0aJYtW0azZs1wdXV96X7/HSQlJWFnZ8fw4cPR6XS4u7tTpkwZPvjgA0xMTKhWrRoHDx5EpVIRFRWF0Wike/fu/PzzzwwcOJBp06bx3XffERKygY+/mI6iclNa9RhYPC/PE6s5d9OwsHUAQJNzH6VKTePeH+NcvgoWNqXY/t1n+AX3QKFUkponUclOxLuUfO7/plH0PDx69CjOzs6YmZnRv39/du3aRZcuXQDw9vZm48aN7Nu3j+bNm/Ppp5+yd+9ePv74Y3Jycvjoo4+Ky8rIyPzzKCZPnvxP9+GFLFmyZPLgwYP/6W7IyMg8hkIQcBDVVFdYUl1pRTWFJbWV1tRT2lJWYYbpS0QSlik5BoPhiUBKI0eOpFy5cri4uFCzZk1WrVpF1apVsbW1JTs7myVLluDn50dOTg7z5s2jTp061K9fn1atWvHhhx/i4ODwSBuCIOAumuKutCNbsOOGZIpWkjAIAgWSgEYwITLsEmErj3B+3QFmDxlN13fbc+HCBT744ANUKhUGgwFPT0+cnZ2fOo4iS1VRICWAG8fPULNMOS4rdEhWFrT+eBjpl66yb/Eq8nNysS9TGnefKkRv/R3T8m6YeLjSPqgpx1ZuYPWqVZw4cYLY2Fjq169PuXLluH79Os7OzpQtWxaj0Vgia6itqMJHYUlF0RwLFFiLSkqJasqJZjRS2RGotMVRYfKnvAYUgoCXwhxvhSVmiCRJWrLQo8FALgZMUNBAaUsLVSnKK8yf2lbRnVxRFIvXxMGDB9m2bRvr1q2jdevWNGrUiPv379OzZ0+USiW2trZcu3aNt956q8Tz8XcQGxtLTEwMMTExuLq60rVrV0JDQwkLC6NOnTqsW7eODh060KBBA/bv309iYiKjRo0iKyuLa9euYWVlxdGjR4mIiMDKyoo+ffrQt0dXspxqkpILFupnjzPt+iVWf96LU7vXkXnrGs6e3gCc3htClcC3MbWyxalsJWL3b0ZUKLDzrE6e1sgwPzUWalmwvmkcP36cbt26ERMTQ1RUFKampnz44Yf8/vvv3Lt3D39/fywtLblz5w5bt25Fo9Fw+fJlhg8fTsuWLRkxYgQ+Pj7/9DBkZP4nmTJlSvLkyZOXPP5z+UkrIyPzpxEEAQtBgZ2owkpQonxDNsH/VYosCDt37uTevXvY2tqi0+n4/fffi91wBwwYwFdffUXHjh1xcnKiX79+jBs3joYNGxIXF4dWq0WSJKpUqfLMdgRBwFU0oaWyFO+ZVaKRmS/eaj8qmtSmgqY8m0Z+RblcOLJzD87WtmRkZLBy5UrWrl1LTEwMkZGRREdHo9frn6i7SCwVCdWzZ8/StGlT+vTpQ8jsBTS9pWNQncaEzf2RiG2/02vFt5RtXI/Dv4WSfD+T9NspGE9coMbZW7QwcyY4OJjZs2cTGhrKqVOnmD9/Ps7OzlSrVo29e/cCPCLyS0IpUU1dlS3NVKVoripF/QfW19cp8qwFJXVVtgw2KcMQkzK8b+LGUBN33jMpTQ2l1XOjJguCULwWir5+/fXXbNy4kW+++QZfX19MTU3ZuXMnY8eOZcOGDVSqVIl9+/aRmZn50vPxVyBJEgkJCYwZM4Z58+YxaNAgJk+ezJIlSzhw4ACpqanUqFGDgIAAtmzZwvHjx+nVqxcajQalUknPnj1JS0tj4MCB3Lhxg19//ZWZM2cCoBAFenurMVFCpubZLtDnj/xOuZqBDPh2G/m52exdOh0Tc0tMLayJP/J78TwFdBzE4V+/Iy1Pom91NQ7y3dU3ipiYGAAiIyP57LPP2L59O8nJyfzyyy8kJSXRq1cvVq1aBYBSqeS9997D1taW33//vdizxNHR8R/rv4yMzLORn7YyMjIybzAGw5MRZdetW0e1atX45ptvGDVqFBs3bmTixIns27ePW7duAfD2229z/vx5YmNj+eabbxg5ciTOzs6EhITwyy+/YG//4ruXRRF1RVFE0Gj5ddJXLBk2luz9UQQ5eDDo/QGcPXu2uJ+CIHDixAlKly5NXl4eCoWC+Ph47ty580TdoiiSn5/P//3f/zFhwgTWrVvH1KlTOXjwIHfv3iV0829UVlhwZetefpm/kBH1m2N6PZXM6DNYn7mGcCeT0B9/5sLx0xw9epSkpCQWL16Mv78/bdq0YdKkSbi5uVGzZk2SkpKIiop6o+9tioKAqaDAUlBiIojPtagWkZ+fz9SpU2natCmzZ88mKSmJKVOmIAhC8SGBQqEgNDQUOzs77t69S2pqKhYWFpw6depvG9vTkCSpeM1UqlSJq1evEhsbi5eXF1u3bmXJkiV06dKFzMxMtm/fTvPmzRFFkerVqzNs2DAEQWD+/Pl8//33BAUFsWfPHs6dO0fHjh0faaeUmcAIPzUKEVJyCiMmn9y1lviIXcVl4g5uwcuvEaYWVgT1+ACFUsWJ31cT1OsjTu1eT979TADKN+yApDKlqUUSNZ3+nVc/nsXD0aTfZJ4V6XvLli389NNPpKWlkZiYyLRp06hTpw4+Pj4sX74cd3d3OnXqhFKppFWrVrzzzjvcvXuXRYsWsX79+uce3MnIyPzzyIJVRkZG5g1k7dq13L9/H4VCgSAIxYFEtFotR48eZeHChezdu5fu3bvz5Zdf4ubmhpeXF5s3F6bvSE5OxtbWltmzZwPQqlUrxo4dS8WKFZ/brlarZcOGDeTm5iKKIgUFBcWBbSwsLOjbty+TJk1i7dq1DBw4kJycHM6dO4dCocDR0ZGuXbsSFBREUFAQgwYNYurUqbi6uj6yGdbr9YSGhtKrVy/0ej2lS5dm7ty5lC5dGldXVwIDA7lw4QIajYbKlSvz5cefMHXAcITsPHp17op3mbIAdO3aleHDhxMQEMC5c+dwdnYmLCyMbt26YWVlxfXr1wkMDESr1fL111+zatWqf8Wm/Gk87DqdmZlJTk4Oy5Yt4/z580ybNo3c3FwGDhxI3bp1sbOzIzo6Gp1OhyiKjBgxgiVLllCtWjWqVKnCli1baNas2T8yjvj4eNzc3FiwYAHZ2dkkJiYyYsQIMjMzEQQBo9HIokWLEASBFi1aUFBQwP79+0lOTsbO3o5Va1Yyc/5UGrcJJLCVP4t3z+brNV9QtUF58o25T23TxUJktL8JnrYKrt/Ts/WbccTu/43U6xcB8KrdmDP7NgFg4+xO1YatOB/+O64VquPi5cOhXxdwMfEW189E0Kh2NdrW8frb5uvvoiiatEaj+XsalKSnBnJ7/kv++AxkZGRw8OBBjh07BoCrqysnTpzA0dERURSpVKkSW7duZf78+Tg6OhIVFYUoiixZsoQOHTqwbt06vLy8nsjJLCMj82by3zoilJGRkfkXk5eXR3JyMl5eXqxcuZLLly8zduxYhg0bhkqlYvLkybi5uREaGsrAgQMBCA4OJiQkhA0bNjBq1CgWLFhAzZo1cXFx4bvvvqNu3brPbTM7O5sZM2bg5+dH165dMTEx4fLly/Tv358KFSpQtmxZWrVqhYW9HZXfasC3y5ZzT6tB62oPpR1o1eYdvv32W3766SfMzc2ZPXs2Bw4coFGjRpiYmJCjNXA9G9I1RtLS0nBydMTcqOH4qdOcPn2akJAQRFEkJCSEffv2MXjwYHx8fIiMjGTZsmWsWrWKhQsXkpeXR9WqVVm5ciWbN2+mdOnSbNu2jfPnz+Pt7U3Tpk3ZunUrer0eT09Ppk6dSmpqKqmpqeTm5lKxYkVatGjxxtzZLAkPR7QVBIGkpCTCw8NZsWIF06ZNY+3atfz4449Uq1aNwMBAOnXqxObNmxkyZAgrV66kSZMmuLi4MHToUDp27IiNjU2JAmCVBKPRwH1jBnlSYaocC8Eaa7EUovh0AZCfn49SqcRoNOLj48PGjRtZvXo1KSkpaLVamjVrRp06dZg4cSKnT5/Gzc2NkJAQOnXqxPbfQynr60zNoKrs2rCfoHfq8eX0zwtddQXINd4ny5jOLS5jJdriqvTCQixMWaPRaNDr9dhbWTG0porFW47iUM4b0a4MF05F4+BREZ+gtuxePIW7KTewc/GgVBlPrB1Lcy3+LDV6TuDiwU0cmtkbIe8uo0eP+k+KnKioKBYsWIBSqaRTp07FwdleG1lZcPw4xMbC5cuQnQ2iCI6OUKkS1KoF1arBY2mnHv8M3Lp1i0WLFrFz5046d+7MoUOHWLt2LfXq1cPV1ZXLly/TvHlzNm3axOLFi2nTpg1ffvklBQUFrFmzhipVqsjWVBmZfyFy0CUZGRmZf5js7GxMTEz48ccfmTRpEoMGDcLDw4N58+Zx+vRpfH19uXv3LkePHsXf3x+NRsOBAwdo27YtANHR0ZQqVYoWLVrQpEkTKleuzKiJE9C7OZKEltvGfO5JelQIj9yJjIiIoGPHjpiamtKxY0fs7OwQRZFff/2VzZs3Uy8ggPZjRrAqNpKNS1ewZ/duKnRqRft5U9C4O3Dmfiq6sk78MnEGbXp0xcnalrt377Jp0ybOJSYTr6zEuotGtuw/xsIpY9n4y89EX0njisqTe1YV0CRfQlAoqVW9KnZ2dnz77bcMHjwYe3t7bt68SWJiIhqNhiVLljBo0CDWrl3Lu+++S40aNcjLyyM+Pp60tDTq1q1LgwYNUKvVxMbGcvjwYVxcXPD390elUjFq1CiWLVtGlSpVcHV1xdzc/IXpTV43RslItjGTZP1V0gxJpOtvcs+YisaYg0owQYnqif4UfZ+dnY0oinh7e0FHvp8AACAASURBVKNQKJg/fz5Vq1Zlz549pKen07hxYwRBIDk5mStXrjBo0CC++uoratasiaenJwAWFhYolX/ujNpoNHJHn0is9jDxumMk6eO5Y7jOHUMiN/UJXCs4S5r+BipMMBdsyM3NLb5nq1QqEUWRy5cvc+3aNQ4fPkxGRgb16tXD2tqa5ORkzM3NqVixIiEhIYwZM4ZNmzbSuk8zNq/ZRumyLkz7/v/4cOIQGrUIRCEqEAURURBRCMoHc6hGK+WRZrhF8q3bfDVpLl9++SVOTk74+PggCAIq7T1MJC1ONhbcupWE0qM2SnsPbl84yZXY49j7NkertOTk1p+p2qA1XQIr8nGXRrRsWIcvv5yGv7//n5rDf5qnBdq6efMmEyZMYMCAAdSvX5/x48fj5ORE1apV/3yDubmwYQMsWgRnzkBODlhaQqlSYG0Nej0kJsKRI3DwIFhZgbs7hgfBxB7u66lTp+jduzcVK1Zk9erVBAUFsX//fmJiYjAzMyMzM5O6devi5+dHuXLlOH78OGvXrqVdu3bMmzcPM7M3Mx+6jIzMHzwr6JJsYZWRkZH5CzFKBrSSBiNGRMTCjbVQaEW4ffs2t2/fZsWKFcyePZuRI0eyaNEioqKiCAoKwsHBAY1Gw0cffcTFixdZsWIFW7du5YMPPsDPz4/t27eTnZ1NWFgYHTt2xCBJpJkIpLzlw5eaq2SjR6LQ7U5AwAIl1ZUWlE64w83jZ0i6cYMvvviCLl26sH37dm7fvk3z5s0ZN24c9zV5XDOV2FmQjtrelnI+VajSOJCA7h0xAnvnLaa0V3lqvRvMoC3L2WNrIEd3l2sHD/Pdjz/RfVU85nfBXHOHa7uX06h9X9yr1mLtpPdR6+7RYuhUrrj78uNvh3Gp25ZWbd5lzJgxLFu2DFdXV2xsbPj000+xsbEhPT2dHTt2sHPnzuJNtLe3d3FApTlz5pCVlUWjRo1YtGgRXbt25fLly7z//vsMHTqUYcOG4erqyvDhw7G1teXs2bNPREX+q9BLBWQabpOqv4GeAkQUiA8S5RRIOnK5R5o+CXPRBieFOzYKx0c26UuXLuXixYvMmjWL4cOHc+DAAUqXLg3AqFGj6NmzJx988AF2dnacOXOGtm3bIooi69ate6H798uQa7jPqfwwsqUMQECBCmVxwh+QkJAwcs+YxvpjS7l05DbHt8ezYtkKqlSpwhdffMGCBQswNzenUqVK6HQ6atasSU5ODvHx8QQGBhIXF8eyZcsIDQ3lWGQEt27d4mDYYX498BM+NV9sFRMEATVmJJ6/wifvf8FbLYPYvGUTFmaWxQcU27dvp1xZDzp37szUqVOxi/2ZKrXqEzxhNO+1b0blzsFo7qXjYZrP2AY2eHgUbpNsfX1f21z+kxQFkDp06BDVqlWjVKlS7N+/H29vb/Lz85k8eTI+Pj7UqlXrzzd2+TJ8/32hddXNDR63TAsCmJsX/gfik5KIHD+etm+/jcPo0WBtzc6dOxEEgYCAACpUqIC5uTnZ2dnFVu6ZM2eydOlSVqxYwd69e+nXrx8KhYIaNWowc+ZMVCo5rZqMzH8B4d9wn8ff3186fvz4P90NGRkZmRKjlTRk6pNJM9xEQg/8IUIuR95izfcbSbx6gyVLluDr60tKSgru7u5MmjSJCxcusH79ekJCQpg6dSqxsbEA/Pzzz8TFxTF16lRiYmLYvXs3cXFxfPLJJ9QJasjq/NucMWZjQMJUUmAqCMXiJzc1g029PsAoSRRk5+If3Iy7ESdp9lYTDhw4QEpKCj4+Pmi1Wn7ZvZ1vInbz68jP+GJXCKKdNRF79nFw4TJM7W3JjL+MhYsjjSd/TE50LMeXradyw3qUbhKIafn67Bw2CnMTNVXqtySgwwB0mlyunDxM2LKvsHcrz424KN79eC7Hd/yCNi+Xiq0G0vrdjpjHbWD9ml/Izs6matWqXLx4kW3btmFnZ4fBYMDJyQknJyfGjx9Py5YtcXBwYPr06axZswZBEPDx8UGn0+Hg4ICDgwNmZmZcvXqV+fPno1ari1P+DB06lH79+v3lFlatMY+rBWfQShrUmD01R3Nm2l0+GzyVYf/Xnwo1PXBQlSHxWCoGvZGmTZuyb98+1q9fz4ABA6hQoQJ16tQhPDycMmXKADB48GC0Wi0nT57E29ubWbNm4eHh8VrHlq6/xcn8vRgoQIEa8RnhL5ZP2cjGhTsRRZE6b1cj49Z92rzdjuNHTnHixAlMTExwcnIiIyMDHx8frl27xoYNGwgMDMTe3p7SpUsTEBCA3lCAuZuIdSkrerz/4lyYj1vLN/y8hez72fQc3R4b0QEPhU+xwFm5ciVlypQhMzOTTz75hKysLJYuXUqHDh3YsmUL4eHhREVFMWPGDIKCgl7PBP6DGAyGR1yYQ0NDWbFiBTdu3KBixYr06tULFxcXAgICGDBgAB9++CHe3oVpfTIzM7G3t3+1hhMSYNasQoupre1zi8beucNnYWHczMqidYUKNLWyolz58kxMSyPx1i2qVKlCYmIia9asITIykuXLlxMSElLs3p6bm8u0adOYNWsWmzZtokOHDq/WZxkZmX8cQRBOSJL0hCuL7BIsIyMj8xqRJIk7+kQSC2LJk+6jwhS1YIpSUKMU1EQfPM3sifNp0jmA/5v1CdXK+3H4UDgDBw6kffv2BAUFMXHiRFq2bEnjxo3ZvHkzgiDg6+uLKIpERESQmZlJ586dadasGb1798ahbBkW5ScRZ8zBQlJgISpRPSRWATISrnDsqx8wtbTAb0gvyo3uh9IIl8KOoBBF+vbtS3h4OMeOHeOOmw21WrcgYV84N+6mYfCvQtb9LOoO7oXaxITqPdohKhXsHPo5Fw8eRbCxIOP6Tc7vjSDp/Bm8g1txaXcoep2Wmi06YzToObByNo17j8bDx58TO9Zw9WQ4leo2JSn+OLqMm9gHdOF+di4pseEcPHCAdu3acezYMVJTU/Hy8iI5ORmNRsOYMWM4duwYM2bMwNbWljlz5uDk5ERUVBRKpZLLly8TGxtLs2bNiIiIKA7es3z5cmJjY8nIyGDs2LG4urr+petAa8zjku4ERgyYChaIwtNF3uUL1/hx5jLupWehzdZTpoYDm9ZuZfe2fXTr1g1PT08iIiK4c+cOLVu2JD4+ntOnT9OyZUsAWrduXRwZecSIEdja2r5WsZplyCAmfydGjKgwKbaoPsymhbuY0GkOZ49cwM3LhXZDWnDjQjJGo5GLlxPIysih3bvtuHr1KpmZmVStWpV27doRGRmJnZ0dzZs3Z9euXdy8eZNSpUox9It+1HunBn61/F7Yv6hDJxBFESsbSwoK9CgUIjFHThJ18ARmanN+27iNmKjjHAoLx9/fnyVLlvDNN9+QkJBAx44dqVKlCt7e3nh5eVGlShWaN2/OwIEDKVu27Gubw3+SosBpCoWCdevW0bNnT+bOncvXX39NXl4eq1evxs/PD71eT/PmzWnatCkpKSlMmTIFtVr9alb6jAyYPr3Q5dfG5oXFZ4SHU9XRkTWdOtHM0xPPMmW4cfkyoXFxHDh5knbt2xMeHk5WVha+vr6cO3eOnJwcfH19KSgowNTUlICAAHr37k2jRo1eYZZkZGTeFOQ8rDIyMjJ/MZIkcVt/hWTDFUywwFSwRPFYHs0bl29SybsiXft0RmeawyXNKRo3aUy5cuXYsWMH1tbWtGnThnnz5gHw3nvv8cMPPwBQs2ZNevfuTZcuhVYnURQpkIysyr/FRWMeNihRPSO35t0rSQhKBS3nTSRqzhK0cVfQlnXi6r10qvn6sm7dOpo2a0aFOn6ELfgJE0SqjX6P6GXrWFevPRkxZzGxMKdS2+a4+PlQkJNHQU4uo2/F0OdQCKU7t8ap6Vvc3r8fPGxR29iSlZHCucPbSb1+kfycLMpUrUXGrWvoC3RIkpFm739GUM+PKF+zAaUt4dyNNEzs3YpT87Ru3ZpDhw5RqlQpUlJSuHDhApcvX+b48eP06NGDHj16kJycTG5uLmlpafj4+ODi4oK7uztJSUmUK1eOgoICGjduzLx583jnnXc4e/Ysfn4vFkJ/BoOk52rBWSSMqIXn35tTqZR4eJbhvY968cOMZdyKT8O/ZTWMSh379u0DoFGjRkRGRnLx4kXGjRvHjz/+SEZGBlCYtsbDw4Pq1au/9nEYjUZO5+/HgB4lz3atzM/RkpetwauGB98dmET3j9swZEZPEs/fIicrjxxdFlu3bsXR0ZFOnTpRunRpmjdvjlar5bvvvmPs2LFERESwb98+Nmxfg7WnGlMsS9THnKwc/m/Y9Ed+1mdEN+o08uOHmcvISs8lMeUyO37fweTJkxk4cCBr164lPDycSZMmYWNjg1arLU6V8l8LqJSamkr9+vVJTEwkODgYW1tb8vLyAHjnnXeoXr068fHxDB48mPnz59O5c2datWqFJEmvJv4kCVasAKOx0Lr6Ak4lJxN56xajAgIAMDx4H5JMTCgrSdx6EPW8bdu2hIeH4+npSWBgIMuXLwcodvm1sLB4PXduZWRk3khkl2AZGRmZ10SGPpkb+vOYYYnwDIva/h3hrP9pM34B1Um+eQeVuYLy7p7o0gVSUlKK0300adKEsLAwHB0dad26NQsXLnzqhiyuIIvvdUlYoUT5HMta1q0UfqoZTO+wtaScOsf9G7cwc3IgNz+P7M37aRIQyKhvv+adVq1I2BdO/x2rsA6qTdaZBGzLumFq96ilxGg0MtvGh/eObcHeuzI7v/wBtY0F+it3yLl5E/egIC78tIqy1QPo+sViVn/em9TrCeh1OnLvpfPh8nBuxEWz6auRBHQcjE/jNqhtnNi/ah7ThnWhjIsjqampfPLJJxw5coSVK1cyd+5ctFot9+/fx9LSkmnTphEVFcXhw4f59NNP6dKlC9OnT+fq1ascOXKE4cOHo9fr6d69O5UrVy4WI8YHAV3+KjKL1oFgXaLyHer15oeNczkaFkXStVu4ejgjmMCVI7dZsXwlRqMRT09P+vXrx5QpU1izZg2dOnXC1NT0LxsDwDVdLPG6SJTPcQOGwvkMtu1PBd9ytOrXmB3LDhDYphY59/M4dfAcmtx8Stk60rldV1avXo1Wq0WlUuHr64uXlxdffvklJiYmiKLIFd0Zco33MXmB0C9CkiSCPN9h87FVOLo4oNfrUSqVxa6wer2eAoUGF7E81d3qEhsbi4uLS/HvCwoK/vX3HB93+83MzCQqKopGjRphaWlJp06dqFWrFhMmTGDq1KmcOXOGTZsK0/iMGTOG6tWr079/f7Kzs4mMjKRhw4avHqDo4sVC62q5coV3VF/AzawsemzaxOauXXG0sMAoSYiCwJEbN9hw5gzvurnRfONGdIC/vz/h4eHcvXuXY8eO0blzZ5RK5b8q8reMjMzzeZZLsGxhlZGRkXkNFLoCX0ON2TPFKkDDFgE0aBFA1KETOLo4YKoy52zcWc7Fn+PKlSscOHAANzc3KlWqxPLlyzE1NWXfvn1PFauSJBGmz0REeK5YBcjPvIedV1mST8TiVrcm18IiiJ63FI+3AridlkpUVBQt69Yn/eJVKjWsx31JjxkiLjW9nxCrUGjdrdGvC1v7jiIp7jLXN4Vy93Qcbh1bkhwdjVurZogmaq6djuD7QU25cvIwVRu04uM10XQYN4+vO/lw5eRhek1fjadfA1Z/3htra2tsy1Zl5uxv2Lp1K3379gVg4sSJZGVlFae/mTNnDnZ2dmzfvp2RI0dy+/Ztjhw5gsFgQKPRcOfOHfr370///v2ZPHlycXRdo9GIJEl/qViVJIlUww2UlCx9TGpyOgFN6mBqZoJvvWocOxjD4q9X4lnFg+MnjzNnzhyGDx9Oo0aNaNKkCQC9evX6y8WqJEkk6uIQH/x7HqIo0qJHQy6dTuTwlhgGTutGr0/a0WNMWzJT7iOKIukZaYSGhpKenk56ejoVK1Zk/fr1zJ07FzMzM0RRRCdpyDZmoKbkYxMEgbFfjmTB1Ec9yIoEnFKpRI0Zuw7voEmTJsU/L/r6bxarRQaHorFotVoADh48yLp164iJiQFg7Nix/Pbbb2RkZDBo0CAOHjzI0qVLiYqKKn7eAFhZWdGiRQvMzMwwGAyvlrN43z4wMyuRWAVQiSI1nJzYf+0aAMYHbTb08MDawoKvjh9n6bRpdOnSheDgYKysrChXrhw9evRApXoysraMjMx/E9nCKiMjI/MayDbe5YruZImtagAFBXpUKiWXribw2/d7kTQK7ty5Q2hoKFlZWdi+IFhJsiGfmfnXUEkC6heIMENBAYtrtESfl4+JjSU1+nQmLz2T/DwNOXfS8Hd2JyxsP92nfIp7l2CSJR3mzxHeAPr8fOaU8sWxXm28enUhNSKSjNh4BJ2Bd9at4crydZz8eQn1Ogzk3dFfF7/OaDAwsbkLXx5IK/7ZgveC8GvZhRodR5J6cjfnNswmMjISS0tLVq9eza5du4iMjGTJkiX4+PjQpUsX4uLimD9/Pr/99ht79+5FqVQSHBzM8OHDH3GR/TtT2OQa73NJdxxTrErUpl6vp0/zoej1erLv59ChTxsy0zJBKMxd6lmqKh4eHgwdOvRv6P0f5BjuE64JQYHqhYIVQJuvo43jQN7uE8TIOX0wNTfhzJELrJqxGWt7S25fu0OgXyOcHVyZM2fOU+u4Z0gjsSAWM+HFrqQPo9fraeAezMEr2zEz/0PsXrlwjejDJ9i5MQxNvobPxnxOj669XqruN434+HjCwsLo3LkzLi4uAOzfv5+ZM2fi4eFBkyZN6N27N5999hlOTk4MGzYMU1NTmjRpQnBwMOPHj+fjjz9m165dtG/fHl9fX7p16/Z6OqfXw9Ch4Or6ZETgZyBJEnOPHeNSRgbzgoMxf3CAkKvTEXL+PJYaDYcLCqjapQvDhg17Pf2UkZF5Y3mWhVVOayMjI/M/wV/uBmq4jfgSj1Sj0YhKVVj+0JYoMrLTWP/Tb8TExCCKIjYlCFZy3pCLFiPmT4k++zgKlYqyb9Uncd8RnH298WwZROq5BI5+/SO21SvRas4XNDSbQilUHDdkYfKU4DqPozQ1xat1U+5lZOHVtR3lO75DxAfjSYuIIfPseap/MJRO/WY+8TpRoaBpv09YO+l92n08F3MbO8pU9UNQKNkxYwB6wYQqpd1wd3cvDii0a9cuTExM2L59O25ubtSrV49Dhw6Rm5vLggUL+O2333BxcaFFixZPtPc04fhXidhsQyYCQonrViqV+NSqQtLVW4QcWQEUHmSEbTtIKXcbOjbog7n4cgLudZBpuA1IJRKrACamavwaexO16xSanHx6jG3L4s9/JSUxjSadA2gzuDE9WwzCXVX5mXXkGe8jSAIlWHqPoFQqGfxJf36avZIPJw0BYOfGveRk5XL/bja9hnUhqH0dyqte/z3fv4sLFy7w+eefc/HiRZo1a8aZM2dwcXEhMTGRSZMm8eGHH1K+fHk6dOhA1apVqV+/PocOHeL06dMEBATQoEEDvv32W4YNG8bw4cM5cuQIo0ePxtHR8fV9FlJTC++uvsQ9YEEQ6Fm9OhP276f/li183qgR59PSWBgTQy0XF74JCqIzgCxWZWT+p5EFq4yMzH+aIqH6sFj9K8SK1qhBUcJHqiRJXE1I5PeQfRzYcRhXdxf6j+uKSq2kYcOGwNNF1uPcR49YwrIApSqWx75COQw6HZHf/IQuO4e3v52M7Vt1yFEoMSJhQMIIKEpYZ/Dir1noUZcLP68mcWMoJo52BC2eT3nfBuhUeqQs6Y9cnZKENjeb6G3LMbexJ+7gNspUrcXZsE3k3M3g9J4N1GjagTq9J9NUPE1GRjq5ubksXryYNm3acOXKFQ4dOsSZM2eIi4vDYDCg1WqxsLCgT58+xX0qyeFE0ZydOXMGg8HwevJOAgXoEHi5wD0ubk54eLkDhfcRVSolwZ2aky/lYET/Wvr1suQZs176NRNXf0AH92Ekxt9kZNAkBIVIz3Ft6fNpBwrQkmu899zXayUNYgkOX55Gr6GdaezZBnNLMzatDMXG3pppP3xORW8vADRSNnoKXqnuN4Fff/2VevXqsflBEKKCgsKxxMTE4OfnVxyIbdSoUcyaNYuVK1cSHR3NggUL2LdvH+np6XTr1g1JkqhQoQJeXl7Mnj2bWbNmvb5n4b17JXYFfpjSVlbMe/ttFkRFMSsigvtaLVPfeotmnp6FQZyuXy8Uwn/hgaOMjMybjSxYZWRk/tMUCZejR4/y3XffsWDBAhwdHV+5vuTkZCwtLbF6LAJmocwr2WZNEAScSzvh5OrA9MVf4F2zMhopG4mXu6JRIEklbhNAbWWB2sIcnx7tKMjNQ21pAcB9SY+BwrqkB6MpKSa21thW9yZxYyj15kzBulolTPQqBK1QXEvRAYEgCESHriT02/E06Doc/za9id66AlNLa+xc3XEqWxF9gY7EmN0EfNyZiAN7yc3NZc+ePWzatIkLFy4QERFBZGQkTZs2pXbt2vz222+0atUKJyen4naeJlaLAtMUlTly5AhXr15l3bp1fPHFFyUe74t4mXVQhIWVOeYW5kiS9ESU2n/q2s7LrkUAS1sLqtbxIuV6GmN+HEiLHg0frfEFY3mVNoswNTOl28COHA2LZvaKqVSr9fid7xe3/6aSk5PDypUrOXjwIAA6nQ61Wg0UPkuuXr1aXHbIkCHMnz+f3NxcPv/8c2bNmkVycjITJkzA3d29uNz8+fNL5MXxUjyI8Psq2JqaMrFxY3J0OiwfjA34QwD/S987GRmZ14MsWGVkZP4zSJKE0Wh8ZNOfnJzMihUrOHr0KEOGDHllF7iYmBjmzJnD7du3WbVq1ROCVSGo0UlaeE76jyKMRiNWNpZ0H9TxQb8LRY74kpY5S0F8qS2+38AexeMuEqsARsASJbkYUDwQrSWdIwXQdPt6zNSFfdcLRgRJwIiEwiiQlngRQRBx8KiAIAi4VqiOmbUd2ekpuFb2xbVCNWq37oUm+x6/zR6FVSlnUm5ex3pCT/z9/YmKiiI/P5+yZcvyzjvvMHv2bGxsbLCzswMo/j083dJcZG0tWhNF4/ruu+84c+YMGzdupFq1ai8xi89Hiarw/XyJ5dV9UKenuy3DK1sc/ywqwZSX9s0Fvt33xTOs28ILI/8qBfUDwf9qjJ467DlrVngixdS/hZSUFFq2bIlGowEoFqsAnTt3Zvbs2axYsYL+/fsTFxdHcHAw5ubmmJmZMXny5OKyDz8fnZycXn9Hzc3/tLB8RKwCFBSAqalsXZWR+R9HfgLIyMj8ZxAEoViYnD9/HiiMoHn16lXS09Np06ZNcbmSsnv3blq3bk2DBg2oXLky4eHhlC9f/olydqIjhhe4HBoMBuAPq2/R9zrysVU4vrSI9hTMEZAwlHCT+LT6C18rUVWwQABEQcBWUKEtoRRWiKBSKopLCwKoDQoMohHDrXvM7VGLBe815MLRXQBk3LqKUqWmTNVa5GamYV+6LDcTTnNy11oUSiX1e4zCysqGLRvXk5+fT3Z2Nu3atWPnzp388MMPlC1bFjs7u2KXyDVr1uDh4fFInx62pBXN9bx58+jevTtLly4FYMaMGWg0muLAVq/L+mYmWCEJLye6niq0JSMCYCL8tdGAn4WtwolCm+fLzcvTxGphHRJ2CufnvtZCsH4pF+jH37MXfX5MBPMS1/13U/QseBpqtZqMjAxu375dnC9Wp9MVv+bTTz/l5MmTvPXWWwwdOpTAwMBH0tIUCdWHn49/CS4uhYL1dVpDc3LAy+uVXI1lZGT+O8iCVUZG5l9LWload+/eLf5er9czZcoU/Pz8mDVrFuPHj8fe3p7u3bvj6enJ2bNngZKJE71ez/fff8/s2bPp1KkTEyZMKE4lUiSWHsZG4YSAgFF6tlgp2izeTLzNxBEzuXTuCgBGDDgoypR84A+ooDTHSVCj+RNWqXyMOApq/FTWmKJAKxlxFdUPXIRLhoVKQG8EoyAhGgQUehGjwoiztQf+bfpSoNWwe9FUbl08Q63gHuTnZqPLz0OSjChNzLidcJoCrYb2Y+dxcudakuOjGTFiBHFxcYwZM4bBgwfj7e0N/CFKnpaO5OGNeRHp6el06tSJxMREJk+ezJIlS1i2bBleXl7Url2b5cuXv/LcPQ1rhQMiCozSswVISShAg53CBaWgfnHhvwB70RkFKoz8uXEAGNGjQIWN+HxXfFPRsvi+8/N42vv8/PJGBIQ3WrAWPRsSExMf+bkkSf/P3nmHV1G0ffjePSUnlXTSCYRAQg+EXkJHelVQQJqAnzQVECnSxBcFC0VQpAgqghRBkCZIC70GEELoJJAAgZB2kpy28/0REkFqIIi+797XlYuczezM7JzdZX7zPPM8BAUFERoayubNm/Pdf/V6PWazmSlTptCsWTOmT5/OxIkTiYmJoWfPnvfU8TA3+ULHwQECAiC94PufH0pGBlSoUHj1qaio/CtRBauKisq/kvT0dHbt2sVvv/1GdnY2R48eJSYmBo1Gw/bt23njjTdYvnw5S5YsoU6dOgQGBrJp0ybg0ZaYL7/8kqZNmzJ//nxat27Nli1b6NOnDw0bNmTXrl1cunQJnU53n+jVSjo8NP6YMOYfe5DVpH/7d/hm6iIqRJYhrEIpzCIHg+SAo1Tw/WQ6Saau1h0zylNZCIUQmFCopXXFQdZQWetMGhac0WInyZgfIb7vxsVOIjPpGhdW/IK9RY9QFBASzjn2NP+/CRQtHs7tpEtsX/QpB35ZQGDZSLIzUnFwccNqzsG1aAA3Lp4m/uRBrGYTjZs0Zvny5cyYMYOKFSs+8fXkTcyvXbvGmDFj+OWXX/D09OSzzz7jvffeY968eVy9epVNmzZx+fJlxowZw48//ojZbC60wDMaSYOXJgAz2U9dhxACBQVPjX+h9OlpkGUNRTXFCiXok4INH00wsvxo656D5IwOA1bxYE+FvHs8eKGu6QAAIABJREFU73u+kXSTzz+YzcaVW/Itjw/CTDZumqJoXpB79YP4a383btxI8+bN6d69O9OnT+fEiRPAn4tjffr0AaBXr17ExMSwcOFCGjVqxLlz5zCZTEiSRL169YBHW2ufOy+9BHctIj4TNluutbZq1cKpT0VF5V+LKlhVVFT+NdydzN7FxYULFy4wcuRIypcvT0JCAiVLlmTgwIGMGjWK4cOH06FDBz7++GPs7OyoUaMGx44dIy4u7oF179q1izJlyrB3714mTJhAYGDgPW51YWFhhIWFsXjxYuDBVlofbXG0NjsyTLnRUPOsJvu2HyIp4ToA/sX82L/9EJ16tcUiTAhsFNOVe2rBVFXngqekIwNbgUSrEIIMFNzQUkOb6xZbRuOEHTJmFErJjtgAy2NEq81qxU4WkJnKsQmfo1xLx2oncM2wR2OTcXL3pmKTTngFlybbmM6W+ZO5du4PilWoQbHyNUi9fgWDUxHKN+pAjmRHlwHvs2LJYlq2bPlU47Fy5UpatWpFTk4OERERCCHw8vLigw8+ICgoiNjYWFJTU1m3bh2VK1dGr9fz/fffP1VbD8Nd4wuATTyd2DOTjYPkUuB8pIVNKX0kGnSPdXV/FDYsaNARqn+86JAkCW9t0H1iP0+A3f2MLF+wmv7t3kajkalQtdxDLYhCCGzY8HgKD4bnSV5/d+3aRXR0NNHR0UyYMIGFCxeyefPmfMt/3n7VUqVK8cknn9C6dWu+/vprli9fzoQJE/jmm2/y93Pn8Vzdfh9H5crg4pJrGX1WEhOhTh1wd3/2ulRUVP7VqIJVRUXlH43R+KfFUqPRIEkS+/bt4/z587i4uODq6sqgQYNo06YNrq6uxMbGYjab2bt3Lx9++CFGo5ElS5ZQv359wsLCsFofLCJWrFjB6NGjWbx4MTVr1qRFixb3BCZxd3enWbNm7N27l1u3biHL8j0CUQjBtavXif0tEZEtkyXS2fTL77Su8ipLvlnBt9MXE715L+9++BZX45O4kXoNgUIJXSXsZaenHp8iko7een8MyGQ8oaU1V6za0CPRW++Pm5zrXusgaWii9SAVKwZkwjW5ojVLKPftkxVKbls2jYwRBT87HbKsZfOwdzBdTcYj489rimjWGZMxg4qNOuIZWJLs9NvcTryMd3ApnNy88QwsScOBn9Ko7zjG926V38eH8SBrWl75EydO0K9fPz799FOCgoKQJAmr1crevXt57bXXcHV1JTs7m82bN5OcnMyGDRvyrVeFhZ3sQJA2HBNZBXYNNoscJGSK6cs+lzyxBcFe40yovioKtqdyDc47r7S+GvYax8efALhpfDFIjphFTv6xPAF2ZO9xflu9DQBjZhZtu7ZgyPg3cXF1IjPD+MD6TBhx1/jg8ALFf54LM/x5727dupW3336bZcuW4efnx6hRo4iNjaVjx454eHhw9OhRDh48CNy7UPfee+8xc+ZM1q1bl59z+FHW5b8de3vo0weSk3MtpE9LRkZuXa+8Unh9U1FR+deiClYVFZV/LIcPH2bQoEEcPnwYyBUjUVFRDBw4kEOHDtG7d2/GjRvH2bNnuXr1KgDR0dFIkkRaWhrLly+nfPnybNy4EVdXV8aMGUPZsmU5efIk8+bN48qVK0CuKL5y5QohISH3tL9w4UJWrFhBTk4OGo2G8uXLExwczI8//gj8ORG1WCxIkoS/vz8b1m7gyxGL6FZtEBtX/s6Xa/7DuDlDObj7CGt/Wo/GSaFxu3rMH7+UUnZVMYinF6t5hGgdeVMfiBMyt7GSLWwoDxB8ihBkCxup2HBEQz99AKV097YfonGgpsaVZMzYI1Ne44yPpMeMwChsZNosZAkbWZIgCwVjfCLLI9uw7vUhuHgUIfnECU5+ugBh+XOy6uYTRKkaTUg8e5xGvUZg5+hMjjENN99iNO03huCol8m2Qr+Kehy1f7p93tf/OxPzv+bUzSufnZ3NoUOHKFu2LEB+VFU7O7t8d8umTZsSHh7OBx98gJeXF35+fs8y9A/FTetDoLY0OcKIRZgfW14IQY4wIiFRUh/x2Ii6fxfF9WUJ1IRhw1IgS2te+UBNOMX0ZZ74PI2kIUAunXv+HQv1lUuJvNF6MB+/9wWZ6UayjNkUKxnIj1+vYETvcYzoM57hPceyZc124M97wiJMaNDirw19YeL/7lRLFosFWZY5c+YM/fr1w2g0MmPGDEJCQkhPT2fHjh0sXryYRYsW4ejomO/NIcvyPf3P27/91yBu/xgqVIAWLeDSpacTrZmZkJICb70Fzi/Wy0BFReWfwT/sLaeioqJCvhU0ODgYPz8/tm3LtaqsXbuW/v37c+jQITp37oxGo6FEiRJIksTWrVsB6NChA25ublStWpU1a9YwY8YMFi1alF/3l19+SbNmzYiOjmbIkCFs3rwZSZI4ffp0fp7CjIwM+vbty8SJE9m0aRN//PEHAF5eXtSpU4fVq1fn73uUZRmdTkdCQgKzZ89mxYoVbNywiWHvDMdyGyb1mcnAlqNp3qoZn86YQpC2DDM//orF85aBWUarLZx9daE6R4Yagmmoc8cOmVSspAsrGXd+0oWVVKzokYnSujHUEEy47n6xLEkSNbRFqK914zZWcrARJBuIkJwIlRwopnHAXzLgfPkmCZ8vJCDNwvXT54jo2p7Xp46nRGAxbsQdZ9fmtZittnyRWbXV68T8toyA8Mq8/f0+WgyYhMUmuGGxRy8LhlTRU8xFfuTkO+9vixcvZvDgwVy+fDl/Im+z2bC3tycoKIjVq1fnfzaZTFy8eJHJkyfTsmVL3nvvPebMmUPlypULZdwfhac2gBL6isiSRJZIxySy7rMc24SNHJFJDpk4y26Usot8Jov786CcfV1CtVUQgIWcR1pbFWxYyEEApbSRlLOv89CyfyXvuXfWuVFMWwaTyMImLFyIu0SFquVYsn0eHV5vhYOjPQ1a1GXhxtlMnD2KkVPfpWbDalw+lwDk3sNmkdvPEvpKf3vgqrstnpIkkZSUxLBhw2jTpg0rV64kJCSE1157DUmS8j1Izpw5Q0xMDGXLluXYsWM4Oztz/Phxbt68+VCx/UJdfx+FJOVaRlu0gMuXn9w9WAhISoK0NHj3XSjz5AsdKioq/91I/4ZE2pGRkeLQoUMvuhsqKip/M4sWLeLYsWNcv36defPmMXz4cC5fvkxAQABeXl5cu3aNqVOnsnz5crZu3UpAQABBQUF069aNtLQ0ihUrdk99RqORIUOGMGLECEJDQ1m8eDG//PILs2fP5p133sHLy4vPP/8ci8WCyWTCycmJgQMHUq1aNbp164YsyyQlJWGxWPJTqaSnp9OuXTv27dvHyy+/zM6dO7Gzs2PKlCn88ssvAMyfPz+/D3v37qVmzZq8+uqrvPXWW9StW7fQxy1TWDluzeCYLQPjHZdUR0lDeY0zlbTOOD1h8JmbipltlltcVcxIEhiQ2TN3MTcvJ+BfLoyTO3bjEVqCvQuWUDEigl+X/MTGX9cxeMgQytZpQXifz7EKMGglHLRw8cAWgqo0INsmYxUSOhnqB2poVEyLQft4C9i3337L5cuX8wNfSZJEly5daNiwIRaLJX/hYMCAAQQGBuLu7s6KFSt4//336dGjxzON6bMghMAoUrlpvUKqcjM/Dq4ANGjw1ATgrvHBTv7nRrEFSLfd5rRpD7eUJATKfSlvJCQkZDxkP8LtauGscX1snadPn2bfvn33RLadNWsWy5cvp0uPToQ3CeTgzhg2Lt1GdlYO4RVDuXQ2np6DX6NK7UrcvpnK6sXrWf3DOiZ8OZKIGuXIwYgeA8X1FR4p/rMsgmtGQZJR4UqGwGQTaGUJL3sIcJbxcZRxMzy9ZTYtLQ2DwUC/fv2oVasWNWrU4P3336dp06Y0b96cQYMGMWvWLEqVKgVA7969SUhI4MqVK3z22Wc0a9bsnytKnwQh4NgxmD8/V7S6u+daTP8qwBUFbt4EoxHCwqB3byj66BRIKioq/51IknRYCBH51+P/nJB5Kioq/5M8yM0zJSWFbt264eXlRYkSJdi6dSurV6/m008/ZfXq1djb2+Po6MiQIUPYuHEj3bt359q1a1y6dIlBgwbh6uqKq6sr33zzDevWraNNmzb07t0bR0dHjh07xqlTpwgNDaVZs2bExcUxZ84cPvzwQ+rXr0+vXr0oX748Op2O9PR0rl+/jouLC0IIhBD4+uYG1MnOzkaWZUaNGkVOTg6zZs2iV69e/PHHH9SqVYu1a9dSqVIlNm3axJo1a9Dr9Xz00UdERkZSo0YNlixZ8tzG1EnSUkvnRi2d2+ML3yHPdRFyvxMhBJ4aPZ30Ply3ZLP7/Gk+HzuB/b+sJ7BSOS4fPYG9Vsf/DX+fY3MWc2DTFnIyjfj7+4MQJMcdpG/mVgKqNOFSpkR8ukLpmk2w00AlF5kQN5nS7jL2DxCqd/cFcgMp1axZk61bt3LgwAHi4uIwmUzMmjWLtWvX0rBhQ3Q6HYqiEBgYyFdffcXvv//O2bNnWbNmDaGhoc8+qM+AJEk4SW446d2wCSs2rChCQSNp0KBDlv75zk6KsCGw4ab1RWPTkaMYMYlsrHfchLXosJPsMciOFNF4Iu5c48Ou7fDhw5QrV46zZ8/y7bff0qpVK1xcXHjzzTdxcHBg2rRpzJ8/n+OHTzL08/4UDfDEarbh4liEVd+v48DOI7h5urJw+o8oQjD9p//gG+JJDka8NIH4aIs/NCrwlQyFvYk29ifasN3R3HoNyFLuIoLJCmBFIFHSVaZhMQ2l3GS08oPFq6IoyLKMTVgxKmmcPneSuXPmo9VrqdmwKkaRSnB5P94fPQKL1UpkZCRhYWH4+vqyadMmgoKCMBgMzJo1iwsXLuS7tN9d978SSYJKleCTT+DwYdiwAeLjQZZzxawk/flv+fLQtGmuYP23Xq+KispzQxWsKioqLwwhRP5kLDU1FYvFgpeXF3Fxcdy6dYv169cD4O/vz/bt24mKiqJLly5ArrW0TJkyWK1W7OzsGDNmzD1CZ+nSpSxbtozhw4czY8YMYmNjmTp1Kn369GHlypW0bdsWDw8PmjdvzpQpUxg6dCjvv/8+w4cPJyQkhNTUVP744w9KlixJWFhYvqVj2rRpzJ49m2rVqtGrVy+Sk5O5du0aGRkZrFq1Cjc3N6pVq0Z0dDQtWrSgUqVK/Pjjj1y/fp13332X9u3b51+/zWZ7oRaUjIwMfv/9d1566SUMBgM5OTnodLp7+nTp0iW++uorsrOz2ffzrwQGBtI0ohp6vZ4lS5ZgfyOVWrVqcfDgQUaNGkX16tWJiIgAoFmj+jg66qn2BH25+7uTJInbt2+TmJhI2bJl2bFjBw4ODowbN46KFSsihMDOzo7KlSsTGxtLdHQ0devWzXe39ff35/XXXy/08SoMNJIWDVqeIN3oP4ZMJZV48ynM5KBBh6PkipP2wYshQgiyFSMXlOPYSfYE6crgKBfJ/9v06dP57rvvCAwMZNiwYYSHh1OxYkWWLl3KwIEDmTBhAvb29owdO5YDBw6QnJxM/75v0qnO69y0JXI+8TQ3b97E2dMR/zBvhk97C72dDr1kwF32xV3ri14yPLBvWRbBr+et7E60opMlPB14qAgFCSEEiUaFOTEKQS4Sr4Xr8HW6X0xZJTO3LFc5cGo38Reu8O4rYxnx2RBe7deBzHQj27pHEx8fT79R3alRrxre2iCyzVl0796dcePG8fLLL+Pj44O9vX2+WM17N/xrxerdODhA3bq5P5mZuW6/2dm5QtXVNdeaqn8x+YZVVFT+HfwXvAlVVFT+LVgsFmbNmpUf/VKSJBITExk8eDC1a9emZ8+e3Lp1i8DAQNzc3Dh9+jQA5cqV48aNG+zevRuLxUKnTp2oW7culStXzhewWVlZZNy1V+rkyZNUrVqVZs2aMW3aNK5cucKaNWuoUaMGqampHD58GEmSKFKkCO7u7sTHx/Pmm28yZ84cihcvjp+fH2vXrsXV1ZVt27Zhs9lo1aoVkyZNonHjxty6dYuBAwfSv39/tFotixcvZsuWLbRo0YIKFSpw48YNlixZwvLly+nfvz/btm3LF6t5VuUXJVbzgrU4Oztz8uRJ+vbtyzvvvMOaNWvQaDScPHmSNm3aEBkZydq1a2nUqBEnT54kODiYyMhIXFxc8PHxQVEUfvjhB6ZMmULFihWZP38+q9auZMznw/li+USSdGe4YD7OdetlMpTbKA9IkXN30CTIvUcyMjLo3r07//d//wfkBpkxm82ULFmS6tWrM2HCBCA31UdAQEB+EKx/tfvkPxAhFBIt5zhnOoyCgr3kjF4yPDKAkSRJ6CUD9pIzNmHjrOkQiZZzCKEwYcIEtm/fzvfff8/KlSupWLEiJUqUoGHDhmzevJns7GwCAwMZPXo0rq6uHDp0iKZNm/L1119jLzvz9fjvGNJmLFFVGjN59FRK6iMo51yTcnZ1KWNXCx9d8YeK1fh0hY/3m9ibaMPPUcLHUXqEWP3zWtwNEgHOkJwtmHLAzI54S/7zK4QgxZrE/LUzGDliNNNHz6V+4/o4ujjiaO+EVtIjKRpavdKMSlUrUDeqLpKQmTR5Ip9++yFlq5Xi5ZdfJicn5762/2vvZScnCA3NDcxUvjwEBqpiVUVF5bGoglVFReVv4dSpU3zwwQfIskyFChUA2LRpE7169cLT05OTJ0/i5OTE7NmzSU5OpmbNmixbtgyAsmXLkpSUxMqVK5EkiaFDh3LkyBHef/99cnJyGDp0KJUqVWLYsGGsXLkSAF9fXzw9PTEajYSGhlKlShWOHDmCi4sLderU4cMPPwQgNDSUixcv4u7uzsWLFwkMDMRkMhEfH09QUBChoaEcOnSI8+fPExgYyOXLl8nMzGTbtm2YzWbWrl3L4cOHcXd3x83NjZIlS5KQkIC3tzd16tQhLi6O+vXrA/+cqJ55k+HNmzcTHR3Nxo0b8fb2pkOHDqxZs4a6deuSkJCAJElMnz6dLVu20LhxY4oUKUJsbCx16tShRYsWpKWlsX//fhTJxsylU/j15A+M//5tdH4WjEo6WdYMjEoaSdYLnDcf5ZRpD8nWBIxZmUDueNwtfr7//numT5+Ok5MTxYsXJyEhgR07dqAoCuvWrQNgwoQJzJs3DwA/Pz8aNmyYv2ihUngIoRBvjeW69TJ2khM6ya7AdegkO+wkJ65bL7Pv4jY2bdrE3LlzKVu2LBqNBhcXF2RZpkqVKnh4eLBs2TJu377NjRs38hcrABYsWEBKSgp9+/bl2LFjjBwxClc7T5xkVxxkF7SS7pH9uJimMPOIGQH4O0toHiNU/4okSXjaS3jY2Vh+xsaWyzZsipV4aywnkvczccDnWLIVvlr5OXq9jtf6deKXxbn3axE3F3oMepXo3/Yyqt+HvFyzF3FHLxB//grVI2qyadsGBgwYkB/YTUVFRUXlflTBqqKi8ty4fPlyfoRfHx8f9uzZQ9WqVRkzZgz79u2jdOnSpKen4+iYm6Nx2LBhxMTEYLFYaNu2Ld999x2Dho2kaat21GjYgj5DRiBrNNSsWROTyYQkScTGxnLq1Cl27dpFp06d+PTTT4mJiSEoKIgrV64QHx+PImzUb16bmJNHSLFco9fgrly/cY2BAwdSuXJlNBoNQ4cO5euvv0aWZVq3bo3FYmHlypWsWLGCtLQ0Nm/ezIYNGyhdujS7d+9m9erV/PDDD0RHR3Pp0iVCQ0NZtGgRQghmzJhB165d8wMz5VkRX4TV5O4ckACxsbF07dqVli1bsmDBArp06UJUVBTTp0+nYsWKrFixgsGDB7NlyxYiIiJISUnhu+++49VXX6VcuXKcO3eOQ4cOERERwRtvvEHbTq246Xiea5aLBAQEYpCcsMMBvWTATmN/x9rmhL3kzKW4BHq93psO3dqQoxjRaDSsX7+eH374gdTUVMLDw7ly5Qpff/01/fv3JywsjPj4eGRZ5vjx42RmZlK3bl0MBgM//fQTAHXr1iUqKupvH9f/dq5az5FivYa95FzgPbZJCdfJTM9dlJAlGXvJmRx9Ghr7P1MNwZ/PRd7Cw/r163Fzc8PJyYlx48ZRtWpVwsPD2bJlC+7u7gQHBxf4OpKzFL6OMWOvhSJ2BffDvnB0F9kZqQAY9Fo85QxGjhrJgAnvsfdINEXd/GjdpRmnj53JP2fgB32J++McF+IuAeBfzJcft82jffeWfLlsKuOmj+DsiYv8uG0ek5e9R6oxhfPnz98zJioqKioqf6IKVhUVlWdCCEGGcptEyzniLbFcscRxy3qVLJORHj16MHjwYM6fP4+7uzve3t6sXr2aYsWKMXPmTIKDg3nllVdITEzEZDJRtWpVihYtyobftmD0LEfziSv4w+yNb8u30TR5j/XZ4fT/ZgeN2rzC0PdGEBsby/79+6lZsybe3t40bdqUjh07MnLkSJo3b05q2m22H9rMgbRNmEokceHqGdbu+4lTtt2MXzoIxTGbrBwjp0+f5uDBgzg6OrJixQqWLl1KmTJliI6OpkaNGiiKwp49e2jZsiU+Pj4MGDCAl156ie3bt5OSksLcuXMpWrQo69evZ968ebzzzjts2LCB6tWrA3+/RdWmWLhqOcsB4wb2Zv/Cvpw1/Lh/FqFhIVSuXJk1a9YQExPDwIEDuXjxIjt27MBoNLJy5UrKlCmDTqdj5MiRuLi4MHjwYFxcXJg7dy5jx47Fy8uLn3/+GYBPZk2i04gm2NkbcNQUQZY0+al+7ubovuMMfGU4vZoNpHSZ0kz+bgzH03bzeq/ufPTRR0RHR9OxY0cMBgNDhw7liy++4PTp0zRq1Ijq1auTlJREfHw8cXFxAOzfv5/OnTv/rWP6v0SGLYVkWwL2klOB8pce3h3D211HMuDlYZw9dSHfo0CSJFISMvAJ8eTIqT+3A9xN1apVkWWZ1atX8/3339OsWTMmTZrEu++++9SRtG2KYOlpCwJwKYBYVRSF7T98wbTuNdixeBoXju4iKy2FMwd+Z07/hhT1UJCKOjKsy0QURaHbW53JzDBy5mSu6NRoNDRr35AJgz8B4Ob1W7zTbRT2jvb4BhbFarWRcvM2xw+c5MaFNITeSpop5b5gYyoqKioquaiCVUVF5akQQnDTmkisaS/nzUdJtiYQc/oI3y78loMXozkrDlCvZQ1SU2/z7bffAvDGG29w/Phx2rRpQ2xsLBcuXKBRo0ZkZmayZcsWAKo2asOitTtYsOsinsFhtOz+FrUaNMXXSWLfrMGs+/xdNOFNOZrqwOiJk9HpdGzatCm/X8OGDePIkSOcunicci8F8+uWVfz68wYUoeAbVBQvH0/OxlwmIfEq+w/s5/8+eY13Jg7A3t7Ad999R48ePcjJycHd3Z0zZ85Qq1YtsrKyyMrKIjw8nMTERBYsWICPjw+TJk2ie/fufPbZZ4wePZqQkBDWrFlDq1at2LNnT35E4b8LRVG4YDrOruyfOW0+gFGkkZllZM7EHxjUZgy3028zZ9dHDJ88mJCQEMqUKYOXlxdZWVloNBq2bduGh4cHJ06c4OrVq7Rq1QoHBwcMhtyx8fDw4PDhwxw7dowsJYNL1hPoMaDlwS6ZiqIwsu9EJg//Alf3IrR8pSmN29bHUefM5TNX+OPMcbbv2sqcOXOIiopi6dKl+Pv7M3DgQKZNm0Z0dDSlSpViyJAhtGvXjpCQEAA8PDz+zmH9n8ImrMRbTqHDgFQAy+q+bQf5bMws6jWrzeKtc4moUSE3cu4d0Vq6fCkMegM7Dm4mNf02cCdfqtnMpk2bcHFxoW7duly6dAmAzp0706xZs2e6lv1JNs7cVvCyL9h5u3+axcWY3bw26Tt6fbqCkpH1cSjijn+pirw5ezl124ZzeNMBrl25zo9zVlDUz4sGLerw/ZdL8+sY9tEgGrWpD0DSlesc3h3D/M+/Z/UP6ynq50WvIV1Zt+w3utTrQ9lK4Xw5ayaTP/4PSUlJz3TNKioqKv+NaMaPH/+i+/BYvvnmm/H9+vV70d1QUVG5gxAKV6xxXLNdQHMnnYVW0mNMzWL90s1cjkukdoOamCQjiiJQTBKpt1Mxm82YTCYqVqxIVlYWO3bsoGfPnhw/fpwDBw4QWqsF69KCKF+7CcGBfug191obvIJK0bjXe4SUqYhHiQpsX7eSURM/5qvP/kNERES+y2DSzatsObCeFv3qYrC359f5v7Nw8goCQnyJqFOWb8YtZv+mo1SqXY7mPaNQ7Ewc2X6SQW8NYceOHfj5+SGEoGjRoty+fTvXrVhRMJlM+Xtf3dzc2LlzZ36uRCEEer2eRo0a5e/R/TuxWq2cMu3mqnIGjdBjJxsQFsEn//cV1+OTiT+biN6gJywiBLcSBoy3c5BMOurUqcPJkycxGAysW7eOmJgYPDw8OHr0KOvWrWPDhg3Url2bESNGUKlSJZycnEASnDMdBUlCJ9k91CokSRIh4cH0G9aDhq3qcTD6KNcTb1CuShmyM3I4dvAEgWW9Ke5dCq1Wy+bNm6lfvz7VqlUjLS2NuXPn8vbbb1OiRAlatmyJwfDggDoqhcdt2zVSlGsYpILlhN21ZT82m8Jbo/qg02mxWKz3RLnVaGRM2WZOHY9lT/Q+mjdtyY0bN/K3B7Rv356aNWtSq1atQrkOmyKYf8KCs1667z3yKIypN9n63ae0GvQfihYPRwiBVp+7f1dvcCDtynqWfDiNFv27E964Piu/mE/nPu3wC/Jl2rivaNX5JRydHbAz6KlYrRwAt2/e5tCuGN6dOICJQ6ZQs2E1atSP5PSxM7z25st07f8yviU8ObjrKMUDQ+7LH62ioqLyv8KECROSxo8f/81fj6sWVhUVlQKTZL3ATetV7Lk34EmxkEB6v9ONdcs2ERtzBjvJgWKl/KnSIoz0zDR+/vlnjMbcvYvdu3dn/fr1XL9+nSZNmlCzfhPmxWTjaifw9vZ6YLteQSUBOL51FV92KYs18zYlnPHCAAAgAElEQVRj5q/jlde6M3XqVACswoxjMRkHdz0aoaNm4yqM/GoA86OnMnL2AEIrFKff+K6cP3mZuJjz6CQ7UpJTKeLvQIP2NfH09GTHjh3o9Xp8fHw4fPgwRYsW5erVqzg4OFC6dGk++eQTli5dirOzM1arFbjfxfHvIG+/m6IoXLId54aIJ/lcGjfib2G12sjKyMZsMvPqkLZIskTHfs3Zve4wXh5eJCVfZcCgtxg1ahSdOnVCo9FQr149du7cyfLly5k2bRqRkZHMmDGDpUuX0rZt2/x2U23JmMh6aETWuyke+ufku2R4ca5duc7lc/HY2ekJCApg9/7dZIkM6tSpQ2xsLDk5OTg6OjJ48GCSk5NxdnYu/IFTeSBCCG7Y4tFR8IUBbz8vrl+9wdcfL2DcwMlMH/8Vn46eyYlDp5g+/mv+r+NQgkr40/ud7hyJOUy3bt2oX78+BoOB2bNnU6RIEbTawsu0d/a2QroJHHQFey4NTkXIyUhD1tzbF0VRkDU5pN88g2/JEoRVj8C7WCBXLl5l3bLNlCgdzLy1M/D29bxnH6oQgrAKpXLzGhf1YOCYvvyyeB1rl27EL8iXG4nJAFSrVZXTcbG4ezx57mQVFRWV/xXUPKwqKioFwiSyuWGLx15yfqBICwkrTscebVjwxQ+0eLkJcUcv0KZHU5yr+LFq5WrWr19Phw4daNKkCS1btuTs2bPUqVOHm54RKJdtOOkfP8F08fShxydLcfcL5tf5n+FeoSRGo5GPPvqIoDK+/LzoV7oMbov2zqTTy+9eF9KwiBDqt6vJwd+Psebb32jaOYqP+nzJFdNZunbryuxZszGbzSxZsoTExETKlCnDzJkz86P9wp95Qwtzkv2kJCUlMXv2bMqWLUuXLl2wYubI5T2smbOVbav2UDwskOCwQNr3fQmtTovNpuBbzJtr8Tc4ffQcN5Nu4+TsTHA5f/bt30e7du1wcnIiKSmJ33//nU6dOtGhQwc6dOiQ36aiKMiyfEfUXC5Q1Ni8sYqsE8GpY3Ec3n2Mzm+0p2rdCL76ZD62NIkTu89QrVo1XF1dAbC3t8fevoC+nCrPRI4wYhJZGHAq8Ll1mtTgyqVEtv26kyq1K2HKMXPrRgpfTppLQHE/xs8YQVF/b4QQfL1xCrp4T0KDw9A/p5Qmh68r2D3Fo2kx5VCsfHUuxuzGO7g0kiTl54vW65KxmqxIksTCUR+Tk51Doz6vUbFabu7U0uVDgXsXryRJ4kbSTWo0qIrB3o4KVcuwbMEqNqz4nfbdW/LHkVgA9mw9gFMRR/RFVDuCioqKyl9RBauKikqBuG29BkiP3N/Wd3gPPn7vCy6eucyRvceJPXCBiDqOvN7jdXbv3s2WLVto0qQJ06dPB8BsE0RfseHxhPokuELN/N8DQ0pz4mISX34xnX17opn+xQxe6lqPhu1qP7KO195ux40rN/n+05UUDfCkQu1w9u7ex4X4JNq2bcvRo0cZMWIEdnZ2tG7dOv+8POH2IiyqZrOZYcOGsXPnTqKioggLC8tNrbN7OVMmzcHT24Plf3zNzaQU3u88mai2NZCACycvUzTAixP7ThMY4oeLmzMDP+rBmdPnWPHxZi5dusSrr75Kamoq9erVuyeacZ7YzHPttJBDjjAWSNTkjZVfkA8hpYO5dC6eC2cu06h1FM6uzuzZvpfGjRvz1ltvFep4qRQMk8gCns5bQK/X8fqAzrw+oDMWixWdTsuVS4l8/sEseg1+jaL+3vnPDgL8Q3zRa55f/s3zqQpOT1G9wdEZj4AQEs8ex5h6E0dXz/zxuHR0CyER5fAKKsn+tZtp2b87RUqE4OP/6Ojf7l6uxOw7Qb92b5ORlkm7ri3IzsrhQtwlXIo4cyHuEjXqR9KhXzOcNQVzxVZRUVH5X0AVrCoqKk+MEIJkWwL6x7gM6nRaOvVsy44Nu0i+dpNDO49RoU4YLTo1YV+FfZQvX/6e8udTFbKt4GFf8ImyOSMFRWOHc7Fy9KgQTKWe/liFmcfNuf2L+9C8a0Nmj17E50PnYrPasHfSk/BHMm9078/48ePx8fHJL5832X6ROVTj4uK4desWR44cuacfnpXs8Q3yRrGCMSMbT193ajStzP4tR+nxXid+nruR7Kwcrl64RuLlG7Tu1YRje06xaMoKZFliysTPiYyMfGCbfxUvWdZMECAVMJdlvpW1bgTHD51i37aDlChVjGp1K1O+TinC9dULPiAqhUqWko4kZHiGtRhFUdDpcqcWm3/ZhiRJBBT3z7dSAkhCIktJx1XzYNf/ZyXLIkjJEfgX3FAMQIVGHdg87yPWfDGcloMmI0kSWxZMxngrhl6TR+BToiilqlYEIMMMGSYbjrqHT6e0Wi1lK4eRcOEqy3ctBMBisfL7mu0EhQRSplJpAMwih0yR+nSdVlFRUfkvRvU9UVFReWIUrNiwopEev9YVXrEUHXu2wWqxYrC3w2q1gVa5T6wCZFoKNkfOTLnB1kVTmd6jNldij1C6YWcyzQKrYsaGFYnH5zuVJIlGHWvTbVgH/Ev4kJyYwpWz16jdtBqrV6/Od3/N24/2IoVqHmazmfXr1yPLMidOnGD79u2cPXsWobdRs1kkdvZ6rp7PjTLaoF0tfl+xixJli/Hu533p9GZL/rPkfQJK+PDF0LnMHvMdVaLKM//AJ1SpUiW/jcflgbTJFoQkiD9/pUB9zxO+xUOLUb95beo1rXnP3y2YClSfSuFjFqYC51y9GyEEF+IuMWPiN7Sv3o2D0UfpMehV4K9ushos4vl93zk2kKWn31fu4ulD87cmIoTg1xnv8/VbzbBzcOL/ZrxHES/v/HKKoiAA2xNMpXz8vandpAYANpsNnU7LSx0b54tVAA0aLCLnqfqsoqKi8t+MamFVUVF5YgRPntReURS8fDzZHLsKD293skXGQ89XBAWoGfT2jugNDnQc+SUBYRFcyRDYBCAVpJbcCXb7N16iXZ9mpKdkYO+mx0sbQFlDnQLVU9jku07ehRCCKlWq0KhRI8LCwtDpdJQoUYJz587hVdqZSd+9R/SaAyyauoLeIzuz8pv1RLWtic1mQ6PR0KBdrkCs0TSC+HNX8S/uiyxLWKQcFEXJdwP+6yT/r7kh90bvZ+qnU8lKzaZK7UpUj4qkVqNqD+zzw2jUOuqez2rmyX8KBXt+/ookSRT188bb15OP5nxwjxi7p1whtPUoHrPm8kQ4uLjx2sSFpCUn4uTmjUYr41hkF2aTQL7jXVCQRSxHZwccHB0QQtzjcn8vEo8al7xnWUVFReV/DVWwqqioPDEyWkBCCOWxORrzJnMe3u75xzQPeeXYawsmWvT2jtTpPCD/swQYtKCRtMjI2LDBE1pZ83Bxd8FoSkerffJgQs+Du10n7z6WJxy/+eYbzpw5g5eXF+np6YSGhlIsJJATB2Jp0KEWP8/ZwKp5G3FycaTLwNYP3I9aLDQAALPFhKzRPnASnDc5/quA3bllF32GvUZE5QgGdR7BqZg4ajWq9kwWaAFoHpLLVeXvQyPpCrQo9SCcizjRpW+HR5ZRUJ7IS+Np0WsKR7QCFPHyA0CxWVEUiQfd5k+SNadL346PtfgqKOgfEMws77nNe06NRiOOjo6Pb1RFRUXlv4QX7+OmoqLyr0GWZNw0RTGTXaDzFKEgIeEkuz7w78EuMpKUmzuxoFgVgSxD8SIy9pIzdpIjilAKVIckSShYsbOzx0PjV+A+PCs2m+2evpw8eZJx48bx/fffk52dfU/QI3d3d2rUqEFISAgRERE4OTnRoHldbJKVMlVC8fBxo0pUed6a9Dou7s73uPj+dcKsaK14aQPzP99dNm9y/M0337Bo0SJu375NUlISO7fuYt/Ww7za4A28/bwYO+29Z7p2IQQSAjtJjQj8onGQnFGwPb7gMyJQcJCLPLf6nXS5i2BmW+FZcWWNFqvVBVm23HNc4slS5zyJe7JNWHCUcsfl7u0IeefOmDGDKlWqMG/evAL2XkVFReXfjSpYVVRUCoSnxh8F5bF7He/GTBZuGl+00oPDdrrYSVQuquFWwXQwADezoLqPBkedhEbS4qMpAZJSYNFqEzYcZCdctd6PL1xIKEpuH/PEoRCCbdu20aVLF/R6PT/++COTJk3i4sWL+X+HXAtLbGwsGzdupEWLFqRfyyYw1BdXLxd8i3lzbPcpkpNSgIdPlPPaDtSVzv/97rKnTp0iMjKSY8eOkZ2dTdeuXXFxccFOZ+CPg6dZsnMuH84eRWAJf07FxD31GFgw4Si7Ikuqq+OLxiA78Xc5aBuk5xcNV5IkgovIGC2PL1sQzKYiSLI1/7O48+NYSM4BimTDUS6CoihIkoQkSfmLWbNmzeLgwYN8++23DBkypHAaVFFRUfmXoApWFRWVAuEgueAku2HC+ETlbSJ31uilCXhkuTr+Gkw2gaUAVlaLTWARUOuutBKeWj8MkhNWYX5it0AbVpAERTXF0Up/n2tqntX02LFjtGrViuPHj7Ns2TLeeustRo8ezZQpUzAYDKxcuRL4U1DqdDqmTZvG5MmTeeONN/ht42aKeZTELGXTpndTOg9qg5ev+0PbFQLMUjap50xMGf8FJ06cACA9PZ0FCxYghODQoUNMmTKFjz/+mF27dpGYmMjNmzfp0aMHxpQcbqUmcy72AiP6jOfXnzaRnfV0wWJsWPDSBD3VuSqFi73kiAYNNmF9fOGnxCasaNBhkJ6vS2sl78IXrCaTB5KUJ1PJj2yuKYQUV4pQkJFzF29kmcTERN5//31GjhwJwJYtW6hVqxYJCQns2LGD9evXP3ObKioqKv8WVMGqoqJSICRJIlhXFjvJgRyR+UhLq0WYMZNDsLYc9vKjc0wEF5FpUULL1YxcN9/HYVEEVzOhTYiWAOc/X2UG2ZHiugpoZT0WkfP4qLfCik1Y8NT446cLeWy7z8Ldrr95vPvuuwwZMoSePXtSsWJFgoODOXv2LAClSpXC3d0do9GIyZQbVVUIgV6v5z//+Q87duygQ4fc/YJh2ho4SM44FdVTNPDh6UIURZCtZGKQnKjh14wzZ85w6tQpAFasWMGxY8eQJIlTp07RqVMn2rRpQ6VKlTh69CjFihWjd+/eNGrQmKlDZ/P+GxMoFhLI2+PfxN7h0amOHoRZ5GCQHHCW3Qp8rkrhI0savDSBmHl+kWrNZOOlCXjuFvVynhp0cu6iVmFhsThhsTghy2YArAoEuzybWM17J+R6ofigk/RER0fTrl07nJ2d6dOnDwD9+vVj//79bN68mZ9//pkuXbqwc+fOZ7sgFRUVlX8JatAlFRWVAqOV9JTUVybBcpo0JRlJSOiwQ0KDQGDFjIIVHXaU1Efg9ISCpEmwFpuAjRdtOOoEbgaQ/7rvUghSciDLAm1CNDQMun/i660NQsHGRctxTEo2kpDRSrr8lB1C5FpVFWEDCTw1gZSyi3zu1tU819/Vq1cTHBxMpUqVKFu2LAsWLKBTp04AhIeHc+7cOWJiYqhUqRJpaWkIIbCzs7snYq+HhwfwZ3Akg86BypqmHDftIE25iSwkdMKQb8VVFIUcJQtZI+Gq8aS8XRR2DvbUq1ePAwcOULZsWfbs2cPLL78MQNu2bdm0aROrVq3C1TV37/GSJUuoWrUqUz6eyrlrJ8lwTcIe58cG4HoQNmHDhoUQXcVCFy93u1SqFAx3rQ/XbZdQhK3QvxebsCEh4a71LdR6H4SDTqJOgIYdCTb8njIf6/1IZGYUw939D7ItegxaCXdDwe+xu59jjUaDImwkXLyKh0cJ8IZLly5RvHhxRo8ezYULF0hISKB58+Y0b948v47s7GwSEhIK68JUVFRU/tFIBdmH9qKIjIwUhw4detHdUFFReQAmJYsUWxK3lRvYhAUJGQfZBS9NAE6ya4HFjBCCs7cFW+OtnE65s8fzTroam8id5JXxkGkQpCXU7dF1p1mTuWo7R5rtBjkiKz9jhECgk/Q4yM74akvgrQ0udLF69epVAPz9/fOPrVq1irFjx1K7dm3S0tLo1KkTjRo1olmzZowdO5aWLVty69YtZs6cydatW6lcuTI7d+5k0qRJtGjR4onaFUKQartBgiWOW8pVxJ39xrKkwUP2JUAXxsHfj5GYmMirr75KZmYm7777Lr6+viQnJ7NgwQIgV/T17t2bjIwM6tWrx/Lly3Fzc2PmzJkEBwcjhCDReo4b1ngMklOB8nfahBUTWQRqw/HUPr8gVzk5ORgMBbf8/q9zwxrPVctZ7CXnQhP9QgiyRQYBulL3BPp6nmRZBB/vNyFJ4KwvrMULgbNLHEJ3g4oeRQosWBfNXIKTixNtuzZHq9Wyd9tBZn08F4PsQFhIGcaPH4+TkxM1atSgfPnyODk5sX//fjp37szYsWOZOnUqGzduRKvVMnPmTEqVKlVI16WioqLy4pEk6bAQIvK+46pgVVFR+aeSnKUQl6KQZsp9TxWxkwj30OBh/+STRCEEOSKTFOs1skUmCgo6yQ5X2RMXjRea5+SauHnzZlatWsX06dNZs2YNHTt25MMPP6Rjx46Eh4dTpkwZ6tSpw2effcbixYtZt24dv/76a/7527Zt49ixY3Tt2hUvr4e7+D4Ki9WM7U6QGB16LGYrXbp0IT09nbCwMOLi4pg3bx4HDhzg448/xmq1UrduXapVq0bPnj0xGo3s3LmTtWvX0rFjRxo1anRP/UIIrlsvkmS7hBZdrpX9EQJHCIGZLASCIG04blqfp7qux3Hw4EFmzJiBLMt06tSJ1q1bP5d2/ltRhMI581GyRUah7TU1CSP2kgsl9ZWeyiL/tMSl2Jh11Iyvo4TuUflnhMDhdjJON65SJPESLkmX0VgtCEkmp4g7KYElyfT2I71oIFdMEBV2ghJuFuyeIHjUkb3HOXk0lvbdW7Fz4x7WL/+ND78ajd5Oz+cTZtK6fUtert+DCuUr0KRJEz766CMADIZcD4ktW7YwZ84cli9fzujRo2nQoAGNGzcurCFSUVFR+cegClYVFRWV50iea24eR44coWHDhnh6ejJ8+HCaN2/O2LFjOXr0KE5OTrzyyiv50T5v3bpFvXr1mDlzJg0bNryvbkVRninP6cKFC7l27RpRUVEsXLiQOXPmADB27Nj8QE89e/akadOm+Pr6snjxYi5cuMC0adOIjLzv/437MCppJFhOkyOMd9yv9WjQIkm5OXutWLFhRgiBk8aNQG1p7ORnjxL71zGHXMv2gAED6Nu3L66urgwePJgxY8bQvn37Z27vfwmLMHHOfPTOPuNnE605woheMlBSH4HuAXlGnzfb462sPGPF1wn0fxGtstWC5/mTFN+zCdeEcwhJAknGYnBAyDKSEGgsZjSmHIQkkaqxx9igKfW6VCPe9QomkY0Bxwcu1Jw4fIop788gJzuHpu0aEBDsR/NOTejbZggderQmqkM1DFZnTm66zEeT/kNAQABOTk50796d5s2bc/78eZYuXcpPP/3EmDFj6Ny58981ZCoqKiovhIcJVnUPq4qKisozkLcfLU84xcfHExQURGpqKrVq1cLFxYX+/ftjMpmws7OjWbNmTJkyBcgVotHR0URFRTF48OCH1v8kYjUvNc3dZX/55Re2b99OdnY2kydPZseOHfkRgRVFYfz48QQHB5OVlUV4eDgnT56kbdu2NGvWjFu3buXvk30cjnIRSuurYRRppNquk6mkkiMyQYCEhEFywl32wU1TFIP04Mn905A35tu2baN8+fJ4enrm/w4wfPhwSpcuTaVKlQqlvf8ldFLu/vOL5uNkiQzscCjwnlZF2DBhxEFyobi+wgsRqwBRgRpkCVaeteKsE7jeceMtcvUC5VctwOnWNcwOzmQUDYSH3JtWBYwWgZ/WTO2YX9GcWE+p9q250jCYFCkZnTCgRX/Pvb1y4Rpad3mJV/q0A8BstiCEoG6L6mxZt42Xmr6En10JRi/+mCVLlhAWFkaDBg3YsGEDERER/Pzzz1y8eJGff/6Z0NDQ5z9QKioqKv9QVMGqoqKi8gzkTVCPHDnCiBEjSElJoVSpUixZsoQqVapQr149/vjjD8qVK0dUVBSbNm1i4sSJhIaGMm3aNCpUqECNGjXo37//I+t/HHlC9fr16yQkJBAZGYmzszPr16/nnXfewc3NjXbt2tGvXz8OHTpEZGQk2dnZREVFYbPZaN26NadPn8bZ2RngicXq3f10klxxknMDNAkhEChIyIUmUP9qaV6/fj1jxowhMDCQ8uXLU6FCBcLCwujVqxe9evVizpw5+eI1JSUFd/eHp/pRuZ9c0VqZG9Z4rtkuIgkZPfaP3a+sCAUz2QgUfDQl8NYGvdA8u5IkUS9QS5CLzOJTFq7ctlLz0FrK7v4Vs6ML6b7FHnquTeQGeJMkKOupIdDZAUlyBLMZzbKfCTpYHNd+r5DokU6OyEBScj0Mki4lc/VyEv2G90QIhRxrNugENpFD59c7sXvNES4fvkFIw3LEx8dz+vRpfH19MRgMWK1WsrKyGDZs2DN5VqioqKj8t6C+CVVUVFSeECHEfalpsrOz+fzzz/nggw/o168fhw8f5tSpU8yfP58iRYrQsGFDZs+eDUD79u157733MJvNrFq1ik8++YS5c+diZ2eXX/+T8td+pKen07t3b1q1asWPP/7I8OHDadCgAdWrV0eSJHJyclOVDB8+nHHjxjFr1iy6deuG1WrF3d2dSpUq0aVLF7TawlnHlCQJWdI8s1iNjo5m8+bN2Gw2ZFnGZDIRFxcHwP79+1m/fj0LFixg3759rFixAhcXF1q2bEnTpk0pX748ycnJjBgxggMHDhTGZf3PIUsafHTFKa2vhrvGFzNZZCsZ5IgMLMKEVViwCgv/z959h0V1rHEc/57dZelFFAQRVLBgQ0A0FlQswa5YYkmMGjXGQmwxGmuMmmuJMZbYWyKxRWOJPXYldsXeCyA2QEGKwLa5f6zs1RhLrMnNfJ7HR4WzZ+ecReS3M/O+epFDtkgny5SOjvvkVRfAX/sOHlZF3mpYfVhhZxX9y6vpEfMT/rt+5UaegiTZupJtNAdTkzD/rjNChl6QpjP3WvVxUgj1UuPj9NCbL1otFC6McusWzmNn4p/sTTFtCPk0BdEoVtxOvklC/HXyeNuTw33stY7kVxeihHVFyrpUod679fn111/Jysriiy++YMyYMdSoUYP33nuPqVOn4ufnJ8OqJEnSA3IPqyRJ0jPcvn2b/PnzW5b/CiGIioqiQoUKlCxZkpUrVzJ27FjGjh1LrVq1WLFiBdOmTWPz5s3ExsZSt25dmjVrRnJyMt9//z2Ojo9WX33ePar37t3jwoULVKhQ4bHP7d27l+PHj9O9e3fmzZvH8OHDWbduHXFxcURFRTFlyhS8vLwwmUwcPHiQqKgoAgMD+fjjj1/pvXpVcu/15MmT2blzJ/PnzydPnjx88MEHvPvuu3To0IEKFSqQP39+7ty5Q506dRg8eDB2dnb89ttvDB8+HB8fHy5evEhYWBgjR460zB5LL84g9Nw33eO+KZ0McQ+jMPckVStaHBRn7FSO2KmcX3uLqBciBERFwdatmAoVIkWnIjXHxJ0sSNcJTMI8k2qrAVcbhTw2Cnlt/rxYU6ZOh41Gg1qlguRkUKth+HB4sDJBCBPFi5fgmwnjadokAoPBYNk6EBcXh6urKzVr1mT8+PHUrl2b8+fPU6JEiTd9RyRJkv5W5B5WSZKkvygtLY3Q0FA8PT3ZsGEDarWaLVu2MHjwYAoUKMCGDRtwc3Pjm2++Yffu3Vy9ehWAli1bMnXqVJYvX84HH3zAuHHjOHXqFL1798bJycly/tyg+qyweu7cOXbv3s3du3fZtWsXGzduZNOmTWzZsoUOHToQEBDAli1b+O2331i3bh1Go5EFCxYQFBREUFAQc+bMYdu2bbRr1w6VSkWlSpWoVKnSa713Lyp3JjU30Hfr1o0NGzawd+9ePD09URSF5s2bYzKZqFq1KvHx8ezfv9/y+OjoaMLDwwkNDWX37t1Ur14dO7uXL/AkmWkUK5zU+XBS53vbQ/nrYmJg61YoXBiVSkVeW8hrq8bP5flPcSM9netpaQzato3vGzTAP18+yJcPbtyAefMw9u2LolajUqmIjIxk5FejiGjaDCur/wX4H3/8kS+//JKPP/6YfPnM91GGVUmSpCeT600kSfpXMglBgimbDbokFuRcZ252AlE5NzigTyVNmFvBODk5kSdPHm7fvs3q1asBOHHiBC1btmTNmjVMmTKFtWvXcuPGDYKDgzl37hynTp0CoEWLFixbtgyAVq1aMXLkSAoVKvTIst/coKo3Ck4lG1l0Rs/MYzrmndCx8aqe3QeP06hRI9q0acO1a9dwdHTEwcGB999/nx9++IH79+/z+eefc/LkSd59913i4+MZPHgwmzZtIjw8nNOnT6PT6WjevDnx8fGWwkx/Z2q1eRnxuXPnuHbtGtbW1oSHh7Njxw62bdtGaGgoTk5O6HQ6GjRowM2bNy0hPiwsjNWrV5OWloadnR316tWTYVUyu3cP5s8Hd3d4gaW20fHxvLd8OQ0XL8Zeq8XJ2pqdsbEYcv9NeXrCyZOo9+5FpVKxa9cuevfujbOzM+3bt2f9+vVMmzaNoKAg0tPTycnJoVu3bpQrV+4VX6gkSdL/HznDKknSv06cMYvthrvcEwasULBDjRUKm2bMZ+C8RTT7ZjgNa9ehRGI2QUFBuLq68vPPP9OiRQtiYmKIiDBX/XR3d6d+/fpMnz6dYcOGsWfPHnbt2kWZMmXo1asXPXr0eOR5/7j01yQEu64Z2RpnIEMvsFErWKnNKxdP34EdU2dTqMg7bFr2Kx72Ku7du4dWq2X06NHExcUB0K9fP7Zs2ULjxo2pWbMm69evx8fHh7lz57J9+3YmTpxI586d39zN/Ytyl/7munr1Kp988gkpKSmUKFGCqlWr8umnn1KnTh1u3rxJ0aJFKVGiBLz0RmQAACAASURBVBUrVqRu3bqkpKSwYsUKjh8/To8ePWjTps1bvBrpb2vzZrh/3zwb+hdtv3qV4Tt20CU4mOYlS+JkbU2noCBmHj5Mw2LF8HZ2Nq8l9vJi33ffMeyrrzCYTKxZs4aoqCh+//13Vq1aRWpqKvPmzSM4OPg1XKAkSdL/LznDKknSv8p5Qyar9IkYhMBd0ZJHscJaUWGlqHBxycOt0+c5On8Ze44f5fd8ak6fO0uJEiVwdHTkwIEDhIaGsmbNGnQ68969+vXrk56ejrOzM6GhoZaltiaTCY1G88is5sNh1WASLDpjYOUFPbYa8HZU4Wan4GJt3jsnrh/nzoWD+DbqyXeHdVy6q8fZ2Zlq1arh7u7Oli1bAKhduzaXLl0iPT2dr7/+GqPRSLt27bh79y4//PDDn+53/TvJDasbNmzg2LFj7NmzhypVqnDo0CG6devGokWLOHnyJJ06dSIgIICWLVuybNkyWrRoQf/+/WnTpg2TJk1i9+7dMqxKfy4rC7ZtAw+PF3r4xTt3KO3mRsfAQJysrTGaTDQqXhwrtZrtV69iMJkwCQE2NuRXqxnZujU7d+7E2dmZggUL0rp1a6ZNm8aKFStkWJUkSXoBsuiSJEn/GjdNOfysu4UzGrRPaM0xMLgmvuXL4Vncj7zlSiKSUimUnEVeV1cOHTrEtGnTaNy4MUFBQXh7ezNnzhxGjBhBkyZN/tJYVl3Qs/2aAR9H5U8r6abeTmDx0Pa0H78MYZ+P+zlGBlSyxZ5s5syZw5kzZ5g1axYGg4HevXvj6elJnz59cHBwICsrC1tb2xe6R6+TEMLSV9ZoNKJWq1myZAl37txhz549fP/993z44Yc0atSIyMhIAKZNm8bJkycZP348jRs3ZtKkSQQFBbF//34cHR0pXbr0W74q6W9v3z6YNQsKF36hh689f56ZR45QpWBBrqWlYW9lRUEnJ25nZpKYmcnImjUp+GBv+qW4OIp6ecGoURiMxldWdVuSJOnf4ElFl+QMqyRJ/xr7DKloUT0xrALU6tKOGxcuU6llU9YPG0dM9F48yvoTGhpKYmIip06dYsmSJXh7e3PgwAEmTJjwSFh9njcB72QJdiYYKejw52EVQK2xwqNYWS4e2oG9RmAwwZZYPXZ2dhQpUoTY2FgOHz6MRqOhbt26VKpUCQcHB4C/ZViFB61uVCrL7DTA/PnzWbhwIRMmTMDNzY127dqxdOlSy+fd3NywtbXFycmJgIAALl68CEClSpVkWJWez/HjYG//wg+vW7Qodf382BEbi6eDA1ZqNacSEzmbnMzFu3e5fPcuW69cITwqipYbN3Lk+HFIT5dhVZIk6RV5bTOsiqJ4AwsBD8AEzBZCTFYUxRVYBhQGYoFWQoiUp51LzrBKkvSyUkx6ftTdwA2rZ/YG7epZiq/3/8b1s+f5afAoWg7oxZQOPRk2bBjXr19n/vz5jz3mj3sxn2bDFT1b44wUcHjy8UIIdi2aRPK1SzTp+w05OTlsnP8tXcPLkZ6SxMmTJ/Hz82PIkCHP9Zxvwx/vSUpKCiNGjODQoUM0adKExo0bo1KpaNKkCUeOHMHBwQGVSkXp0qX59NNPqV27Nv3796du3br06NHDMisrSX9Jv35gZQWv4I0cvdGIlVrNlZQUvtu3D73JxOpz53C1taVnhQr0rFgR4uNh4ECQlX8lSZL+kifNsL7OwOoJeAohjiqK4ggcASKAjsBdIcRYRVG+APIIIQY+7VwysEqS9LIO6lPZZ7yHm6J95rFLhozm1sUr9P15PjcvXibNWk2/oiHcu3YTW1tb3NzcLGHseXuo5hJCMHhPDg5WYK15PLCaTCYUxTzzei/pBhumDeNOwhXUGg0qh3xo0xMIq1yBQYMGUbBgwb90D94EnU6H0Wh8bJbXZDIxadIktFotrVu35ssvvyQ+Pp5169ZRs2ZN3n//fUtP2B07drB9+3bWr19P69at6dOnD9bW1m/jcqR/usxMiIwEHx9zYaSXYBIC1YNzTNi7l1OJicxp3JhDN25Qxdv7fwfGxkKHDlCz5ks9nyRJ0r/NG18SLIS4KYQ4+uDP6cBZwAtoCvz44LAfMYdYSZKk1yoVc0Xg59FiWH+Ort/CrctX8SzmR17vAtwXJnx8fB4Jq8BfCqsAOUbIMjwaVk1Go/n3B+FXURRuXzmLWmNFxGffYu/sSvypQ2Sn3aFdv1FMnTr1bxdWjUYjN27c4PPPP2ft2rUAHD16lB07dgDm5cDbtm3DysqKPn36cPLkSbp27QpA3759mTNnjmU5dc2aNenfvz/jx49n4MCBzwyrQoh/RMse6S3Izja3sXnJsCqE4FxyMl/u2EHQrFnsiY+ne0gIVmr1o2EVQK2GjIyXej5JkiTpf97IBgtFUQoDQcABIL8Q4iaYQ62iKO5PeExXoCuAj4/PmximJEn/x0wClOcMrFobG+p92oVbl67i4VcEgIfXojzv0t8nj+PBn00mTu/6ldjj+2jcZxyKonD9wnG2zv0PGSmJuBYoQukajWkx6Hs2Th9OHv/KlKtS66We/6961lLns2fP8v333+Pt7U3Lli3x8/Pj999/Z9u2bRw6dAiVSkX79u3p1asXBQoUYOLEicyaNYuwsDAAYmNjadKkiaU9T506dVAUBUdHR6ZMmUJmZqalz+0777zzxPEpikJiYiJ6vR4vL6/XdTukfxohzL9ekqIoeDk6UsDRkflNmhDk6fm0g0G+gSJJkvTKvPaiS4qiOAC/AH2EEGnP+zghxGwhRIgQIsTNze31DVCSpH8FJ0WDgef/IfKDsV8SWLeWubItYP2Kvl3aaECtghyDEZVKhWPe/CTGnuPu9asoisLVmGgCw1vRc84OTEY9O6MmIkwmCgdU5vaF46iy76EoynMVd3oZxgezvk8Kqzqdjh49etCiRQu8vLwIDg7G1taWgIAAUlJS0Ol0HD16lIkTJ7JlyxaOHDlC7dq1KV26tCV4jh8/nsmTJwMQHR1NeHi4ZYb52rVrALRt25bU1FT8/f0fG1tuWE1KSmLOnDm0bduWkydPvrZ7Iv0DvcKl5M42NnwSEvL0sApgMICd3St7XkmSpH+71xpYFUWxwhxWFwkhVj748O0H+1tz97kmvs4xSJIkAfiqbTHyfFV8c5lMJrIw4aJocFVezYIUlaLwjqeau9nmb78ZKcnYOuXh5M41mEwmblw4wdnoDUxuXwWN1oa2I+bj7O5FAf8gBIJL0ebltq9jlvXcuXMYDAYAS3Gj2bNn8+OPP5KSkvLYsTqdjkOHDjF48GDCw8Px8vKiQoUKFClShOTkZACqV69O3rx5OXz4MI0bN8bX15cPP/yQ0qVLc/ToUdq3bw+Ax4MemefPn2fQoEFEREQQHh5O0aJF6dq1K87Ozo+NTVEUUlNT6d69OytXriQqKop69eq98vsi/YM5OJjD40OVqV87lQqeFWolSZKk5/baAqti/mlqHnBWCDHxoU/9CnR48OcOwJrXNQZJ+rcxmUyWvXz/hB7Lb1J+RUs+xYr7f2GWVaVSkYGRCmrnl1sG/IfXpXIBNTfOxzC2eSnO7d1EVnoqF/ZvxWTQo7bScj8thc6TVtN6+BzcChXj2pkj2PgEUtq/BK4Or6f40JEjRxg9ejTr1q0DYPPmzQQGBhITE0NSUhKfffYZd+/etRy/e/dukpKSsLe3R6fTWXqs2tvbU716dby9vdm+fTsA7733Hhs3buT69etMmDCB6dOns3btWpYuXUpQUJDlnGfOnKF9+/bk5OQQExNDZGQkDRs2ZOBAc12+3DYhmzZtolWrVqxevRqtVkurVq0wGo2W8cmvfclCUaBoUUhPf7PPKwOrJEnSK/M6Z1irAh8CtRRFOfbgVwNgLPCuoigXgXcf/F2SpFdApVKhUqm4d+/eG93n+E+gKAqV1c6kY8D4nIEmXRhwQI2f+sWW9xmNRoQQltclKyvLvBfOQeH+iU2UjehJy0HTeLfLEJzdvTi+dQUVGrfHqNdx8eB2EmPP8/Oorvw6+QvS0tL4z6BevP/++y80lifJDdLly5cnMDCQAwcOAJAvXz42bNhA3bp1iYqKYt++fSxYsMDyOGdnZ4oWLUp6ejpardayjxSgbNmyFCpUyFJwqWHDhjg4OJCamgqAu7s7vr6+ACxcuJB58+Zx6dIlSpUqhb+/P3FxcYB52XGHDh3Yu3cver0egGHDhjFr1iw+/vhjoqOj+fjjj2nUqBFubm6Wx8mvfekRZcuaqwW/CZmZ4OwMrq5v5vkkSZL+BV5nleBoIYQihAgQQgQ++LVBCHFHCFFbCFHswe93n302SZL+SAjx2DLNixcv0qVLF2rXrs20adM4cuTIWxrd35Ov2o4qaheS0KEXT59pTRMGjAiaat2xVp7/W6Ver7cEO7VabdmP2bVrV8LDw1m2bBmKouBre5/s62e4ni5wK+yPX0gNTu38lYIly1P9g95cPbGPX8b0xMHDl/pfr6FzBVeKuLz6OnkqlYoLFy7Qu3dvTp8+zcmTJ4mJiSEwMJALFy7wxRdfcOPGDUaMGMHmzZvJeFD91MHBgezsbMue0ZycHABSU1NJSUmhWLFinD9/niNHjiCEYMGCBVSsWNHyvMnJybz//vssWbKE2NhYGjduzJkzZ+jUqRM6nY64uDi0Wi3+/v4UKVIELy8vAgMDOX36NDNnzuT06dNs374dT09P7OzsKFWqFAcPHiQhIeGV3yPpHy7kQYeEB3ufX6vkZKhf/6WrEkuSJEn/89qLLkmS9OoJIYiOjmbnzp0A3Llzh8uXLzNz5kxq1KjB6tWrOXz4MDNnzny7A/2bURSFdzTO1NS4cg8DiUJHtvjfD7EmIUgVehJFDraKitZaT9xUz+7bCnDz5k2GDx9OQkICiYmJmEwmDAYDs2fPpl27doSGhtKnTx/mz5/Ptm3beK9FM5yzb1LU6g53TA4kpmRw88pZ9q9eQOGQOlTpOoZGE7ZQtf1AIis4EeLx8mHV+Cc/sCcnJ9O/f38KFy5M8+bNycjIYM2aNajVarZs2ULXrl0pV64cmzdv5uLFi8yZMwcwt56xsbFhzZo13L1719J6ZuHChRw+fJiqVasyYMAAgoODURQFKysrsrOzuXfvHgBJSUncunWLjRs3MmrUKJo2bcqvv/6Kq6srpUuXZtGiRVy4cIHg4GD8/f3p3bs3K1eu5MyZM1SsWJHU1FRWrVrFhAkTAPOy40OHDnH69OmXvk/S/xlnZ6hcGW7ffr3PYzCY96/+STVrSZIk6cXJwCpJ/yAPV241Go3MmDGDoKAgPv/8c2xtbRk2bBi+vr60b9+emzdvcvnyZTZt2vSWR/33oigKQRonulgXpKbGFVBIFDqShI5k9BRU2dBcm5922gLkVVk91zmzs7Pp2bMnSUlJFClShC5dujBq1Cg0Gg2ZmZmcO3eO9u3b06JFCypXrszBgwfx9PSksI83u6b2pWTcCsSVvVRq/glOhUqjM0FRNzu6lbPiyyrWlMqnfiXXnlus6OjRo5YqvImJiZw5c4ZevXrRuHFj+vfvT0JCAgcOHGDRokVMnjyZxMRE1q5dS2RkJBUqVMBkMuHi4kLHjh25desW7dq1Y8KECVSuXJk1a9ZQpEgR8ufPT0hICIqikJaWxpdffkm5cuXo1KkThw8f5s6dO/j7+xMTEwNAREQEO3fuRKvVotVq+fXXX8mXLx82Njakp6czZMgQfH19+eCDDyhVqhQjRoygUKFCHDlyhDFjxlC8eHH69etHjRo1Xsm9kv7PNGpknmF9sBLgtUhIgIYNzQFZkiRJemWUf0JxipCQEHH48OG3PQxJemv+rBfmoUOHaNGiBdWrVycqKgpFUbh//z79+vUjPDyc5s2b06VLF4QQzJs37y2N/O9PCIEBgQnQoKB+zqV8D78mGRkZjBw5EpVKhUajYdiwYdja2hIfH4+dnR3t27enc+fONGvWjN27d7N48WI++OADQkJCWLp0KevXr6dbt27UqVPnNV4prFixgqlTp2JnZ4dWq2XkyJEUKFCAfv360alTJ2rWrMm5c+fo0aMHvr6+uLi44O/vz8KFC0lLS6NXr1506tQJg8FgKYCUlZXF/v372bhxI7Vq1XqsSu+2bduYN28eTk5OzJw5k7Fjx3Lp0iUCAwOZMWMGTZo0YcyYMQgh8PLywtbWFj8/P1QqFePGjePGjRuMGjWKvXv3IoQgJyeHqlWrUq1aNa5cucKVK1fo378/HTt2fK33Tvo/sGULREVBkSKvfsnunTvg6AgjRoD2+VZlSJIkSY9SFOWIECLkjx+XM6yS9Df1cGXZ3GA0Y8YMwsLCmDJlCm5ubvz8888ULFiQ33//HYC4uDj27t1LvXr1yMjIICcnh2PHjnHhwoW3dh1/d4qiYKWosFZUzxVWL168aHlcrps3b7Jq1Sp++eUX/Pz8sLa2pkOHDgwfPhxXV1fq1q3LypXmzl7Vq1dHpVKxc+dOjEYjH330EStWrHitYTW3Wu+lS5eYMGECGzduJCEhgW+//ZabN29Svnx5Bg4cSPPmzbl+/TparZadO3eSmZlJly5d2LlzJ4MGDSIqKgr4X7VeABsbG8LCwhg/frwlrEZHR9OxY0f27NmDt7c39+7dQ/vgh/hevXrh5OSEh4cH3t7erFmzhj59+lCtWjVsbW05d+4cGzdupGnTpowaNYr69euTlZXF5s2bURQFGxsbNm7cSIMGDWjTpg2nTp2SYVV6PrVqmQswJSTAq3yzPiMD7t+Hrl1lWJUkSXoNZGCVpL+ZQ4cOAf+r+JsbWidMmMD69esZPXo08fHxdO/enbJly2I0Gi2PKVmyJIGBgZY+l9WqVWPz5s0UL178rV3P/5OjR49Sq1YtwFxcaerUqZw4cQI3Nze++eYbmjVrho+PDwCjR49mxYoVZGRk0LhxY+7evcv69esB6NGjB927d8fBweG1j/nQoUMsWLCA+Ph4+vfvz4ULFwgICKBSpUrEx8ezdOlSdu/ebVka3KxZM0qWLMmcOXPYtWsXYP5aVKvVXLx4kVWrVgHmGWaj0fhIdWAhBP/5z3/4/PPPCQ0NJTg4mOLFi1OzZk2cnZ0tM85OTk5s3LiRQYMG4efnR05ODh999BE5OTlkZWWhVquJiIjA2tqac+fO0a1bN0aOHGm5Jnd3d8LDwy0Vk/9sb64kPUathp49zTOs1669mtCalgZ370LfvlC48MufT5IkSXrMqy85KUnSCztx4gR9+/Zl0qRJhISE8NNPP7Fv3z7Gjx9PXFwc3bt3JzQ0lNDQUPz9/Tl//jw1atTgt99+Y+bMmdjY2DBs2DDS09MpWLAg7u7ub/uS/vHOnTtHoUKFsLW1JTg4mNDQUDp37sz9+/e5c+cOW7ZsoXPnzkRERHDy5En27NlD2bJl8fLyonbt2gwdOpRJkyYRFhZGdnY2AGXKlHll4zOZTI+ERp1Ox4YNG1Cr1TRu3BgbGxvi4uLw9vZGURTWrl3L+PHjCQ8Pp1WrVvzyyy/cvn2bhIQEjh07xo4dO8jMzKRChQr4+PgwePBg2rRpw7Fjx2jYsKGlNY2iKJY9sStWrLCEzI0bNxIVFUW+fPnIzs5GURTq1avHuHHjmDVrFv7+/qxbt446depQo0YN1qxZg5eXF/Xq1SMmJoaFCxcSGRmJo6MjOTk5qFQqunTpwu+//05GRsYjIT93WXbuOCTpmezs4LPPYNYsOHrU3C/V1vavn8dkghs3wMYGBg6EEiVe/VglSZIkQM6wStJbl9urE8DHx8fS+gRg586dfPzxx9jb23PlyhVLL0qApk2bMmvWLBo1akTlypWZPXs2ycnJ+Pn5ERwcLMPqK7Ju3TrGjRtHZmYmK1eupG/fvqxevZrIyEh+++036taty65du7h+/Tp16tQhJSWF3D33w4cPt8yqfv7557Ro0eKVj0+lUqEoCvHx8SQlJSGE4O7duwwYMIDr169TtmxZVCoV27dv59SpUxw6dIjIyEg++eQTVCoVZcuWJU+ePJw4cYLQ0FCqVatGVlYWMTExzJ07Fzs7Ozp27Ei+fPmYNm0aH330keW5d+zYwTvvvENUVBTOzs4oikLFihVp06YNffr0oUuXLgQFBVG8eHHS09P56aefGDJkCGlpaRw7doxff/2VDz/8kFOnThEXF0eLFi0YO3YsBw4cYPLkyWRkZODi4oJarWbhwoWPzUjLfqvSC7G1hV694JNPIDUV4uPhwZtJz2QyQWIixMWZ2+X85z8yrEqSJL1mMrBK0luSG1Jze3UCuLi4ULt2bS5cuEB0dDQqlYoiRYoA5oA6Z84cyx5Ke3t7qlWrBkDbtm05evQo/fv3R6WS/6yf5lmF5v64vLRMmTKMHDmSGjVqkJKSQmBgIF5eXpbqtqGhoZZAWLFiRbRaLXv37iU9PZ2goCDL6/Us2cLILVMO8aYsrpuyuWPS/+lYc5eI5zp69CitW7emWbNmREZGcvDgQTp16kSTJk0YP348mzdvJigoiMzMTAYNGkSrVq3w8fEhKioKW1tbVqxYQefOnZk0aRIAISEh5M2blxUrVuDi4sKQIUM4fPgwvXr35o7KyOWcdC7q0kkwZbNr316aNGnCmjVrLEulx4wZw2+//cbUqVNZtmwZKpWKY8eOkZaWRkpKCt999x0XLlygV69ejB49mqCgIOzt7Tl27Bg1a9Zk4sSJ/PDDD1y6dIk5c+bg7u5uuQ9y6a/0yqhUULUqjB0LERGQnm4OodeuQUoK6HTmNjV6PWRmwq1bEBtr/ry/P3zxhTnwOjm97SuRJEn6vyeXBEvSG2Q0Gi3LF3ND6saNG5k4cSINGjSgTp06VK1aFT8/P1q0aME777xjKVbTtWtXLl++zIgRI4iNjUWtVrNgwYK3di3/FJnCyAVjJseN6aQLIyYE1qgorLYlQO2Ap2KNoiiYTCbLXk0wF1Ly9PQkOzubcuXK0bBhQzp37gzA4MGDmT59OpGRkZQrV459+/axZ88e6tevz3vvvYe1tTWOjo5/Wt35YUIIkoWek8YMThszEPwvoJqAvIoV5dVO+KntsFZUCCG4jZ5r+ix+W7uO4BqhbNu+mfCIJowIDKZ169b06dOHLVu2MGTIEKZNm0bnzp1JS0sjNDSUpUuXcvDgQaKjowkICDC3t7mbRKV2LVnYdBk/HokmoFwAQTWrE3/iNIqikCWMXDJlsSXuHCvHTiIzOYXiNatQreuHXC+enxU9B7Nq/VpM2ToKFy5M3759KV++PHv27GHGjBncunULLy8vJk6cSPny5Tl69Ch169aldu3aTJkyhZiYGMLDw/nxxx+pUaMGrVq1omXLlpY3Xh6+h29i6W/u14H0L5EnDzRpAvXqwfnzcOUKnD0L16+bQ6taDS4uUKkSFCtmnk3Nl+9tj1qSJOlfRQZWSXoDoqOjmT17NoUKFaJKlSpUrlwZFxcXRo4cyf79+xk5ciQxMTF8/PHH7N+/nwYNGnDgwAH0ej3t27fHz8+PatWqMXLkSC5fvkxaWhqVKlV625f1t5YljOwxpHDWmAmAI2ryoEEBjAguG++z6ffdJB4+yYD2nQjI5wXAokWLmDhxIn5+fgQFBTFo0CCKFy9OWFgYX331FUIIGjduzKxZs1i9ejURERGUK1cOk8mEEIKKFStaxvC0sJojTGzSJ3PFdB8rFJyxQvPQ8UIITvx+gBmLl1OuUTj1a9Tinp2GowcPcmTxahIvXCbDKy9KaACj+w7n1slzePv4cP/+fX7++WfCwsI4ffo0tra23Lhxg8TERAAmT57M0KFDKfduGBVLBxA58ztq9uuKR+VgVqz/lcUrlnH2t93YaLWsPbQX3+Z1KVghkB/a9yS0TXPeadGYL8rXwj1PPhJPn8U2Xx4y7LSEd+uA/aUbfP/995w+fZrs7Gxq1qzJhx9+iJeXF15eXpQuXZqbN2/i4OBAZmYmrq6uuLu74+/vj1arpVSpUoB5mbMQAiHEGw+Puc+3f/9+nJycLGOS/s9pteYKwmXLQtOmb3s0kiRJ0kNkYJWkV8go9KQak0g2JqATWRzafYx5YxejFlratGpLRnoG3377Le7u7ixevJhOnToxYMAAZsyYwZIlSzhy5AgbNmygQYMGrF27llKlStGxY0dWrFjBypUrqVmzpvwB+jmkCwOrdImkCj2uWD3WrubCrn2sHjsJIQTWTo58HHOcrh0+IqyIuSDQzz//jFqtply5cgQEBNCwYUN8fX2ZOXMm3bp1w8bGhtatW/Ptt98SERFB5cqVqVy58nOP774wskqXSLLQ4Y72sWB7ZtdeVo+ZhKJS8HunPHuWruTA/gO8N/xzbuw8SMzyX5kWewyNlRU/DRiBRig0HzeMkM5tODRoAgsXLqRWrVq88847xMbGsnr1akqXLs3x48fRarVk5HdhUWY8Nu6unN+wneaRXek8/iuun7vI0Ep1ibqfQKwpizUTp5G0djMVwqozYFUUCWfOM7d7f6ysrYle9DPHN+9g/t1LWNvZsXfrdtYsWMSoz7/Ar0BBpk+fzrZt23jvvfcsfVtnzJhBtWrVCAgIYPfu3WRlZeHk5IStre1j/VsfLiT1Oj286gHg8uXLzJ07l8OHD9OjRw/5702SJEmS3jIZWCXpFUkx3Oaa4SwmTGiwRoMNhmxBfGwCa09HYaVoKawNoGfPnnh7e/P7779TtWpVvv32W/bt28fBgweZMGECI0aMoEGDBlSuXJmffvqJDz/8kA8++IAPPvjgbV/iP0K2MLJGl0i6MJBP+fOeiPqcHBKvxDHp/AEMOh3rp8xm8ca1VB8Xxrx585gyZQorVqygXLlyLF++nIYNG/Lll1/y6aefcv78eZKTk5kyZQpNX2AmxiAEG3RJ3BU63J4yvttXYvnuwgEuGDPJWrKSnFt3uCly8I8I5+CqdZzbsx8HVxfORe/Hr0IwGXE3sE/XEZd1j3sGHUlJSYSHh3P6veLo/AAAIABJREFU9GnOnz9P6dKlyZs3Lx4BJenfvSeK3kCtjz4gsH5trKytAShcrgweRYtw8eoV7vrkxb1AAS6ducxFXTqeGXrWjJ9Kg15dKV0zlP5lQ3HxcCc9+S5zhw/g9I49FChbkltlvGnkXZYxY8ZQrVo1tm3bhsFgYMCAAVStWpV3332X/fv3M3bs2LfabumPFYazs7OxsbFhz549TJ06ldOnT1OoUKG3Nj5JkiRJkszkRh1JegVSDLeI1Z9CgzW2iiNWihaVoqJ6eBV0OXoyk3QI4Ez6fhRbIx999BHjxo0DIDY2loiICACsrKw4fPgwx44do2XLlixbtgw7O7u3eGX/PEcN6SQLHXkUqyceE/BuGAa9ntRbiWi0WjAJbFUatplSOHLiOHv27OHw4cNs3bqVtWvXsn79eurXr8+IESPw8PBg0qRJ5MmTB09Pz788vivG+1wzZePK08dnNBq5dPsG+5etZmefkWTfSeHG9r3c9/OkYrNGHPhlLY55XbGysSGwbk3iTpzmyyr1STx2lvjYq0ycMY0SJUrg7+/P3r17MRgM2BRwx69/JyIG9GLgr4uo1aUdrl6PXkO7CV+x7MuxWKMiMeY0Tm55ybDVcD09lcyUVPwqBgOg0mgoVK4MAwLDSLoaR61OH9Bv8Rx07s6cNWSQN29eAgICGDduHLa2tnTt2pUTJ04wcOBAdu3aZQmrb6uQUu7s7YwZM6hcuTLDhg3j7Nmz1K9fnwoVKrBjxw4ADAbDWxmfJEmSJElmMrBK0kvSiWziDWexVuxQK48uWlAUhc9G9WTyiJlYKdZoFCuu6k7S//P+lhDh4eHBjh07CAsL4/r16yxatIgyZcqgVquxt7d/S1f1z6QXJo4b08jzlDAI5tel9chBrPx6IrcuXeHMzmi8ivqiR3A16x7p6elkZWVx8uRJXF1dOXnyJDqdjnbt2jFw4EDy5s37QuMTQnDEmIa9on7qcldFUWg7ZhhfFa7I7d2HaPrjJLyrhrCpyxekJt2haL0wUm8nknDmPD5lS5KdkUnn78cxZPNyGvXrwdCY7ezetYtLly5RtWpVrl27xp49ezhlzEAFVGjwLo55Xf/0uQu/W42rW/ewoHwDTEYjFSI7Yo2KuzYKxSqFMCHiQ0bWiqByqwg+mjIWKxtrei2eTcsvB2Bjb4cTGo4a0zl85AjJycnUqlWLYcOGMXv2bAICAqhVqxbu7u6WKstvopCSEOKx6soAc+fO5dSpU+zYsQOj0UhkZCSpqal06NCBzZs3A6DRyIVIkiRJkvQ2yf+JJekl3TXcRiAeC6u5GrYO5z/9JzL428+ws3MgS6QTc+YQgYGBpKen07dvX/bt20dsbKylCq30Yq4as8gRJpxVTw+sAFXaNGNOt8+4cf4SdXt2pkLT+mQKI0kezlSqVImQkBC8vb2ZMmUK9evXfyXjSxZ6bosc3PjzpcAPK9m8HlY97WgwbbTlYy6+3pxfvIYCvT8mv29hYo+fwr2wD2d27SW4UTjWdna4enliEoLC1Stx4Voc1Su8w9ChQylWphRzcq7j/Iwwf0voqD1xOLEbd/LuhKGWj9sU9KDuV59xasmvlK5ZDbdCBQFoN34Eq/7zHZ2njUelVpMZn8CxI0cpFlSVSpUqYTKZUKvVuLu7o9frsbKy4tixY2+0h2rufthbt25x5swZypQpg7u7O+np6fj4+PDVV1+xfft2OnXqRPHixVEUhc2bN7Nu3ToaNWokKwdLkiRJ0lsk/weWpJcghCDZGI8W2yceo9Fo6DqgI7PH/wCAGg0btq6jVq1a5MmTBzs7O2rXri3D6itw3pSJjfJ8M3ZqjYa2/xlKqRpVqNC0PiajETtUaIoUYODXI5k3bx6bNm16ZWEV4JopG3h69eBcdxQj7wzszu6vvrN8LH9ASfL6eJEi9DQZ2IumA3pRpXUzjHo9M7v04erRE0Qv/oVxjdoCApcyxXBwcKBs2bJcN+VgQjxSifiPTAhShYHSzeoTu2MfugxzhWUhBCoBqRqFsI5tcStU0FIVuUqbZhxZt5nLh2KYFzmQCc07kHE7CUOh/IwePfqRVkFWVlbPff0v4+FlxkIIsrKyGDJkCOHh4WzdupXWrVtz9+5dLl++zLJly3B3d+fYsWP06tWLnJwcvLy8KFu2LJs2bQKQYVWSJEmS3iI5wypJL8GEAQN6bBWbpx7Xrvt7VC/SkLz5XVmzaAOu7i70m/rFGxrlv0emMGLF84eh2l3bE1kkmGaD+6LK7Y8rwKBWXkvboExhRCNUPM8QdQjKd/uQWcWqY5/fjbMr1mPt4oxPqLltjq2rCwB5CnjQ7puv2DxtHhsmzyIjJZWmA3rhVi0EK42z5XzZmB7q8vrnDAgUQG2lIejjthyeEUWVz7uZixOhoMO8rPbhdjNqjYZ6kV34ptmHNB/Sjw7fjSZTo5CjMl/km5ydzK34+/AyY0VRSE1Nxc7OjgMHDnD58mXGjh3Lvn37KFeuHHq9nho1agDwyy+/sGTJEubOnUuPHj1wcXF5I+OWJEmSJOnJZGCVpJfwrACQy9rGmladm7F320FGTBtIiQA/fG18X+vY/o0e36X4dFobG0Lfb0HMhi0ENwy3fPx5X9e/SiCeK6zmHq2xsSawU2uubosm/LsvcS/rD5jb4jw8RjtnJ5oN7kt25n1s7M1FupJNOvPzWc7211T9ooclxP/xHH+cIW30WU+afP6p5e8mQw4mxfxqvO6wmpiYiBCC/PnzW4Lq1q1bmT59Oh07dqR27docPHiQ6OhoNm3ahK2tLStXrqRhw4YYDAb0ej1Dhw4lIyMDgMjISBlUJUmSJOlvRAZWSXoJajSoUWMURtTPWIrab1QPFEVBJ7KxVj15CbH04uwVNWnC8JQF2o9r981Xj4Uq69e0W8JO0WB8zuhohYosDNT4qt8jAVEIcwxV/0nyzQ2rACYF7FT/+xb/PNdkOafAElZz27+YAO0T0nbu/TMaDKg1GoRKhb3q0f9e/tjv9FW4d+8eM2fOpGPHjgDs2LGDnTt3kpiYSGBgIOvXr+fkyZN0796dvn37Mnv2bOrUqQPAsWPHsLe3p0ePHtStW5d79+5Rrly5N1IESpIkSZKk5yc35kjSS1AUhXzqgujJeq5jAYxCTz5Vwdc9tH+lYio7sv/iPOvDYTVLGHFUNDg/oYDWy/JSWSMQCPHs0JpXsTIv0f3DbKYOgbOiQf20vagPzl9QsX7kuVWA8SnPrQYcFI1l6S889HWLIK/q6cWi1A8q6hoUgY/K9pHrzA2C69ats1QIfhEPV/x1dnZm+PDhpKWlkZOTQ1JSEpMmTaJ169YMHz6czp07s2XLFqysrGjYsCErV65k1apVfPPNN7Rr145t27YB4OfnR3BwsGWMu3btIi0t7YXHKEmSJEnSqyMDqyS9JFe1JwKBSTy7n6RB6NAoVjipX6wtivR0fmo7rFAwiL+6ONgsHSPl1U6oXlNRIA9Fi6ui5f5zhOo8Kis0KJbwmcuIwFNl/YRHmaVjpJDKBpeHqiXbKWr81fbc42l9RRUKqKzR/2EW2CgEGhRcnmNRTo4wYWNS8FasHwnb+/fvp3jx4syfP58+ffowd+5cMjMzn3m+h5lMJhRFQaVSWcLw1atX+eyzz9i4cSOtWrWiRIkSpKamAlCyZEkCAwOZNWsWkydPJiwsjOXLl3P58mV++eUXPvnkE+B/IXjChAkEBATwww8/WJYIS5IkSZL0dsnAKkkvyVplh5emGNlkPDW0GoQePTkU0pZB9ZyVbKW/xlpRUVbtQMpTQ9mf0wsTahSKqe2effALUhSFELUTGTz7zQ0VCh4qa7IwWTaPGoRAgwqnpwRHkxBkYyRY7fTY5wLUjuiFwPCUWVZnRYMGxTITK4QgGxOeivVzBfl76KmodUGjUpGamsqXX35JfHw8y5cvZ+jQoaxcuZL+/ftz5swZVq1a9czzGQz/ey1VKhW3bt2if//+9OzZkwsXLlCkSBHq1KnD/v37EULQqVMnpk+fDoCDgwPNmjXjhx9+IDk5mVatWrFgwQJmzJhBiRIlUBQFo9GIoijcuXOHAwcOsHTpUhYsWECBAgWeOTZJkiRJkl4/GVgl6RXIpy5IQU0JcsgiS6RjFAbzXkMhMAid+WPo8bUqh6Mqz9se7v+18hpnHNGQJvTP/RijENxBT01NHmxf85sJRdV2uCtaUp5jfJ4qaxwVDfcxYjKZg2Mhlc0Tg6NJCBKFjkC1Ez6qxytXe6isqaJxJhndE5cGq1AopLIlCxMmkyALE06KBo+nzOqaHrSRScOAE1YUV2wZN24ckZGR6HQ6fHx8SE9P5+rVqwBUq1YNX19fEhISLMt7/yg9PZ2VK1daWtQIIdi0aRPvv/8+vr6+lClThuHDh3P69GlatGhBXFwcO3fupFu3biQkJLB7924URSEoKIixY8eya9cuTCYT1tbmmd+LFy/Svn17hgwZwtGjR4mLiyNv3rwMHDiQCRMm0KdPH9atW/fkF0eSJEmSpDdCBlZJegUURcFN400p68p4qH0xYSCbDLLJQFFUeGtKUMq6ilwK/AbYK2qaad3RoOKu0D1zv2iOMJGEjlB1HkqrHV77+LSKiiZaN2xRc/cZoVWNQnG1nflYRY+nYk2+P9lHKoQgUxhJREegxpHqmjxP7HVaUeNMJbULSehIe/DGyh+5qazIr2i5oxiwR00xld1T98yq1GruCT3Jl2OpkaHBQW3FzZs32bt3L2PGjAGgRo0a3Llzh5SUFFxcXLh+/br5sQ8t7wUsAdbR0ZH169czdOhQ6tevz7JlywgODubnn3+mcuXKrF69mtOnT7Ns2TJ8fX0pWbIkO3fuBCAiIoKlS5dazhMSEkJycrLlnhw4cIB+/foRERFBSEgIHTt2RAhB165dqV+/PiVKlABg+fLlnDlz5qmvkSRJkiRJr5esEixJr5BWscHDqjAeVoUxCSMKCooi3xd60/KorGht7cFW/R3iTNmohIIzaqwevBZCCNIxko0JW1TU1+SjpOb1h9VcjoqGVtb5WadL4pbIQftgme8fZ05zhIk0DHiotISonLhj0pMocrBChQYFAegxYcRcpKmaOh8l1PZPDKtgfnOlssaZAiprDhnvcf3B/dGYv1oxCIFRMeGpsqakYk+8KZt7GHASGrSKiuyMTBSVgrWdHUIIMjBy/lAMGwb/B08bR277+/PJJ5/Qp08fVq9ezZUrV/D19aVChQps3LiRzz//nEaNGrF371569eplGVNuv9aHi2DZ2dkxY8YMBg8eTJs2bQA4evQoQ4YMYeDAgWRlZTFnzhzOnj1LkyZNGDJkCKdOnWL06NGPXK+Hhwdt27Zl8uTJ9OnTh6SkJGrVqoWNjQ1TpkzB29sbd3d3ChUqRHBwMADJycls376dkiVLvqJXXZIkSZKkFyEDqyS9JnKf6tvlqGhops3PXZOeM8YMThjT0QvzfkiBuWpuebUTPipbNK+pyNLTOCga3tN6cF1kc8yQzlVTFop4tF+qLWqqalzwV9vjoGgwCkG8KYvLpizuCyMqFJwUNcXV9uRXtE8Nqg9TFIXCalsKq225Y9JxyXifNIwYhcBOUVFYbYuXYoNaUbgn9Jw1ZBJjTOeeMHB8yzYy76ZQpVNbbp65SFDpMmSt38P00WOpVSOMcuXKodfrGTduHM2bN2fixIl8//33FC9enAkTJrB48WKWLFnCoEGDqFevnmVMuUF18eLFnDt3jn79+jFgwAASEhIICAiwHLdr1y5UKhW1a9fmwIEDnDt3jkWLFjF69GgmT55M0aJFH7nW1atXs3nzZvr06cP48eNp3749iYmJTJ8+naCgIIYOHUpoaCgAmZmZbN26lVGjRlGgQAH69+//3PdUkiRJkqTXQ3me9gpvW0hIiDh8+PDbHoYkSf9gQgj0CEwIrFA9dYnr25AmDNw16dFjQoWCjaLCQ7F+6+PM3YttVOCmKYfDR4/SqV5jvH3NxY56de3G6NGjycnJ4erVqxQrVoyRI0fi4+PDmTNnaNmyJdu3b8fDw8Nyvj8LgcuXLyc6OpqkpCSys7Px8PBg9OjR7Nq1i59++okpU6bg5eVFXFwc1apVo2nTphw/fpz33nuPZs2a4enp+UgP1fPnzzNr1ixu377NJ598QvXq1enSpQt+fn60adOGyMhIvvrqK0JCQkhLS2PIkCFERkZiZWVFdnY2pUqVemP3WJIkSZIkUBTliBAi5I8fl2sVJUn6V1AUBa2iwkZRv/UQ+GecFA2F1bYUU9vjp7bDS2XzVseZG1Rz28goRhM+aluSY07jnjcfVYJD+G7MOJycnLh//z4qlYrly5fzww8/4OPjw8WLFylVqhSVKlVi//79lnM+HFazs7O5cOECAAsXLuTQoUMsXryYWbNm4ezszKJFi4iIiCA9PZ0TJ05w/vx5hBCsWrWKggULMm/ePCIjIylYsCBqtZqMjAxOnDhhGfuRI0dwcXGhevXqAHTt2pWlS5fi7e1N8+bNGT58OG3btqVy5cro9Xry5cuHr6+vDKuSJEmS9DcilwRLkiRJj8kNlikpKQwbNoxLly7RtGlTunfvTnBwMN26dUOv15M3b14CAgJISEjg8uXL6PV6+vfvj7u7O1OmTGHOnDmWmc/cc+buV507dy5Lly4lOjqaPn360KlTJ0wmE25ubpQuXZqDBw+SlJRE7969WbRoERs2bODrr7/GYDBw//598ufPj6IoZGVl8c0337B48WLq1KlDamoqP/74I23btuXIkSOkpaXh5OREhQoVKFWqFN9++y0DBw4kLCyMmJgYpkyZgpub21u715IkSZIkPZmcYZUkSZIs7WPAPBOak5PDkiVL+PTTT/Hw8GDcuHEMHTqU33//nZCQELy9vS39TiMjI6latSpTpkwhIiKCihUrMnHiRABLWBVCoNPpWLRoEVFRUQD06NGDnJwcdu7cSe3atQkKCmLq1KkAVKhQAYCoqCgaNmzIuHHj+Pbbb5k1axbR0dFUrVqVnJwcABISEjAYDJw8eZJPPvmExYsXs2PHDgIDA7G2tmbLli2AOTC3b9+ezZs3A+Dn50fLli1lWJUkSZKkvzE5wypJkvR/KEeYSBI6coS5TYxWUeGuaLF+qGq1wWBAo9FgMpkswfLs2bMUKVIEGxsbrl27xokTJxg6dCj+/v707t2bmTNnUrVqVTp16sTAgQPx9/fnwIEDDBkyhIYNG2Jvb285Z24hpbNnz+Lr64u1tTVxcXGkpaURGxtL4cKFadq0KbNnzyYsLIwuXbrw9ddf07t3b0qUKEFoaCg+Pj4A5M2blyNHjjBz5kwqVar0yLVu27aNy5cv06hRI3Jycli4cCF16tThv+zdd3xN9//A8de9N1OWBIkkQsSWIQkZFaSovSnamrWpKjX7U6OD2iNWhaJFqzp8qxWNVbFLiBVKhAwEkUSmrHvP748rITVKjUi9n49HHuLm3M89JzfJOe/zfn/en9u3b3PkyBFCQ0Np164dRkZGtGzZklatWr2It0AIIYQQz4BkWIUQ4j8kSZfHnrxkVuZc5ufc6/yWl8hveYn8nHud4JzL/JGbRExaMj179mTcuHGAvkPviRMnaN++PW+99Rb9+/fn22+/pU+fPvj5+XHq1Cmu6XLwGtab/adP8MXB31Fa+OHbujnzFi7A09OT27dv89577z10zH79+rFhwwZ69epFZmYmB/ceIXYPBNq8x5bNoaydfJhaJq3Jy8lny5YtAHTr1q0wODUyMmLdunWULVuWmzdvsnXrVk6ePElKSgqvvfYae/fuZcyYMezevZuePXty9uxZtFotPj4+VKxYkZSUFADp+iuEEEKUMJJhFUKI/wCtorAnP4UT2jTUqCmNBoO/rQGcryic1mUQodGxded2KtjZExERgZeXF1u3bsXV1ZXNmzcTEhLCqFGjaNu2LaVdKvLt8YOcqedMGXtbHL1c+SUoGOvVC3Gb/B7eRkaU0piRpzZk586d2NnZPXLMsN/DMbxZkR/nHEXt3hYzc2tMNVZ899NqLOJ9aVlmMsumfU/FMrVx96+s3+87meDRo0fzzjvvEBsbS2BgINHR0WRkZHDgwAG8vLw4cOAAZcqU4ciRIyxevJgpU6bw5ptv3peRLSl0On12/N61aYUQQohXjSxrI4QQJZxWUdial0iU7jblMET9tyxi5B/7CP/1d1qPGEw5Zyduxl/hxwVLMbmdRw0zaxYFBfHOO+/Qt29fmjdvDsAbzZrh+1ZHdD41ObhoDR6BAbzWtQPpScmk30wi5ep1wn/9nRYjBmFQqTyZl69zadFauJ2DqakpCxcuvG/M1xs0w8u4Bx62zfnf+WnEJJ3GSGNMvSrNsTaz4/cTa1CjwaNcCzp6jMbvA7BzuxuwarVazpw5g5mZGbdv36Z69ep07doVNzc3PvzwQ7755hu2bduGlZUVY8eOxdvb+4W/F89CwXm5IBucnp6OhYVFce6SEEII8dzJsjZCCPEftSc/hfPaLGwfEKwCaPPz2b5sNaFLvyIrLR1zG2uSz0Th3L4pEXEXSUhIwM7OjiNHjgD6gKlel7Zs3/0HtarWoGx5O0wtzFEUBTPr0jjUqFY45o6lqyiVnoO5tRV7TkXwRod2xMfHc+3atSJjpsaDm7obJ6/upHINB95rNZc33Hswqu2XdK8/htJm5UhIucic3tvo3Wo0JlZwaB4kRYGBgb4YSK1W4+7ujouLC66urhgaGtKyZUt0Oh02NjaMHDmSDRs28N1335XIYLUgo6pSqVCpVCQnJzNq1Cj8/Pz4/PPPOX78eJHthBBCiFeBBKxCCFGCJevyOKFNx1Zl9ND5me5vBGLtUJ687By2f7mGrFupVPOrR3b0Zaw8a7Hs69WMGjWKkJAQ1q5dy+6Tx9iyZQuNOrfHUKOhw/gReLZsWrgm64PGVKVm4uLnTcj5E3h7e/P1118XjvnN12vZOPUURy9vobn3OwAYGZjQru4gHKxdAPCp0oJ8bR6pWTfZfnIdE35qQmz2YQ4vgpSbaUyePJmxY8cCEBERQUREBAMHDmTx4sV06tQJ0AfalpaWz/tb/tzcW/o7c+ZMunbtirOzM1u3bkWr1TJ06ND7thNCCCH+6+SsJ4QQJVikNh01qgdmVguoVCre+WISmbdSqezlzvJBoyhV2pIKNatRJaAe2/eGYWVlxfTp0zl8+DDDhw7FxdcbrxZNADAwNHysMa2tSmNawxm3AH/27t1bOOaebYeZ9+sQ3F38qVel+UP3cWjzOXSeY8fxmN30DpyMR3VfMlJyGdpvFOnp6bz//vsA/PnnnwwePJiqVaty7NixwiVwSnpDpYSEBObNm0dMTAwBAQFERkbi5eVFpUqVmDJlCgkJCezevRu4WzYshBBC/NdJwCrEK6pg3U258C25chQdJ7UZWKH5x219O7fl1M4wPJq9TlUfb/43YyF5ubl4+PiArTXTZ84gMDCQqbNmMHjXD7w5dgRqzaPHvW/MmUEoOXkY16tF+fLlmTVrFoGBgfT2ns3sLvt5p8EENOqHjxlYuysmhmaMbb8ST+fXydfmYW5jxADPZcybN59KlSoB0KdPHw4fPsz48eMxMjJ6sm/aSyA/P7/I/0+ePAnoM6cRERHs2rWLBg0aUKNGDeLj4wu36969Ozt37gRKfnAuhBBCPC4JWIV4xRQEqBqNBp1OJxe+JViikosWBUPVP/8p1xgY0HbUUDZNn0/XqeN4b81iyjlXpJSpKX59uuHfugUASYYKOhQ0j/Fz8aAxKzlX5oKhln79+tG2bVvycyDplDFm5f75eDRqDb0aTWLFjo8AfVBmUhoy4o3IStIVzt00NTX958FeMsePH2fevHnA3Tm5cXFxHDx4kCFDhhAbG4udnR0NGzbkxIkTJCUlMXLkSGbNmsXBgwfJyMjg/PnzsoasEEKIV44ErEK8Au7NoqpUKnJyctiwYQO1atXi2LFjxbhn4mnkKjqeJD/eYvgAti5aAYBny6ZUqFUdgMr+dXF7zVc/purJMu5/H7NS7RpoUfB5zZ/XXnuNvCxQq1U8RkwNQGe/9/n16HIANGoDVCpQqUGbrS6RczcLKhlMTEwYM2YMeXl5gD5bGhoaSuXKlfH19eW7774DoEWLFmRmZrJ9+3Y6deqEmZkZs2fPZtiwYdjY2ODq6lpsxyKEEEIUh5J39hdC/KOCAFVRFBRFKZJFzcjI4P/+7/+YP38+oaGhJbKbqrjrSfLjRiYmNHinC8e2bHtmr/+sxzQyMKGZRy8OnPv1mYz3ohX87ul0OhRFQXOnrLpmzZq0bt2aqVOnAnDz5k169epF+fLlad68OYcPHyY5OZlKlSqRlZXFhg0bSEtLY9iwYSQmJrJ69Wq++uorrKysiuvQhBBCiGIhAasQ/zFZWVlkZWUBd5fHiIyM5IsvvmDPnj2Ym5tTt25d4uPjKV++PCDzWEsqo8dNW96j5+xP8G5zf+Mj4zuhr/G/OC3cO2a+oqBBhcGd8QxL6bdRnmAllvdazqd+jXb65ymA7u44L7Pbt2+Tk5MD6OejqlQqzpw5w7Rp07h69SpTp05l9erVbNmyha5du2JiYgKAl5cXxsbGzJkzh/j4eBRFwdHRkcTERHr27Mlff/3F/v37gbsZWyGEEOJVIQGrEP8xo0aNYvz48QDk5eUxZ84cBg8ejJ2dHZMnT2bt2rX4+/vTokULNm7cCEjAWlKVUxmhQUXeE0SDfy+rzVcU1KiwVeubFzmoTVCjQvsEPxP3jplKHrU1ZoVZfQNjKO8JmYmPPRzqewLx7FtQujKYWD/+84tDdnY2c+bMKWygpNVq+eyzz+jRowcajQa1Wk29evVwc3OjXbt2REZG8sMPP5CZmYm9vT2TJ08mMjKSLl260L59e5YsWUKVKlUAmDt3LqVLlwYozNgKIYQQrwoJWIX4D7g36/Luu+9y8OBBMjMzMTQ0JDedhlyxAAAgAElEQVQ3lxUrVuDp6UlUVBTR0dFYW1vTtGlTQkJCAFnXsaQyVqnx0JiTyr/PuqWSh7vGHBOVPhAyU2mooTEjjfx/eOb9FEVBi4K7xqLI4y7NIP/2v9u/nFSo2gpext5gkZGRTJo0iR9//BETExMmTZqEnZ0dubm5KIrC2bNnWbduHRMmTCisZpg0aRKmpqZ06dKF9evX07NnTz755BNcXFxYu3Ythw8f5u233wbudhPu3bs3Hh4exXacQgghRHGSq1QhSrCCQLUg63Lr1i38/f1xdnZm+fLlZGRkcPDgQTp16sT06dPZuHEjU6dOxdraGn9/f9LS0tiwYUNxHoJ4Sq4aC3Qo6P5FllynKGgVcNOYF3m8jsaCXHRPlGUFSCOf8ipjyqmKrttatgaYl4espCfbv5w0MDIHu5csVrt27RqtWrViwIABGBoa8sknn7BkyRLOnz/PmDFjWLt2LfHx8aSkpFChQgWys7MLmy01bNgQR0dHUlNT+d///seIESNo3bo1xsbGWFpaAnd/rwu6CQshhBCvMglYhSjBCgLVnTt30qVLF7p27QrAgAED2LBhA+bm5lhZWTF48GB+/PFHAgICiIyM5Pvvv6dKlSqMHDmS+vXrF+chiKdkozakjsaCRPKeqLRbURQSycXNwJwyfwsw7VRG+GtK65fNecwxM5R8QEVzw7L3LZWkUoPPe6DNgezUx9u/3AzITgG/D/RlxS+TmzdvcuPGDaZNm8bkyZOZPHky+/btw8jIiA4dOrBr1y4qV67MzZs3OX78OCYmJhgaGnL58mUApkyZwrZt+iZVjRs3xsfHp8j4UvYrhBBC3KUqCXPX6tWrp4SHhxf3bghRrArWoLy3fDcmJoa2bdsSGBiIjY0NQUFBnDx5kkqVKtGsWTOGDRuGg4MDixYtwszMDHNzc0JDQ3nzzTeZPHmylAL/R2gVha15iUTpblMOQ9T/UD+ruxOsuqhL0cawHAYP2F6nKOzPT+GINg0LDCiF+oFr9moVhRTyMEJNJyNb7NQPjy6To+HgXNDmgpkdaAzv30aXDxnX9Z/7fQB2bo8+9uKQmZlJcHAwf/31F8uXL0er1eLu7s6OHTvIy8tj9OjRjBkzhqtXr/L111/j4+NDamoq+/btIygo6L4A9e+dvIUQQohXkUqlOqooSr2/Py71RkK85ApuKhUEl7du3SpswBIZGUmrVq2YPXs2AFeuXOGjjz7i22+/pX///ixatIhdu3ZRsWJFfvjhB9LT09m9ezdly5YtMr5cLJdsGpWKVoblMMtP4YQ2DbWipjQaDFT3N1hKJQ+tAu4GFrxuYPPAYBVArVLRwMAaO5Uxh7WpJCp5GChghBoVKnQo3EaHGqitMcfHwBIr1QMi0HvYVIHXP4HobRCzG3R5YGACagPQaUGbrc/GOgVA1ZZg4fCMvkH/klarfWC208zMjMDAQPbs2cOaNWvYs2cPjRo1okyZMgA0a9aMlStXsnLlSqpVq8a6deswMzNj8+bNlCtXrnAcnU5X2E1YCCGEEA8mGVYhSojExESmT5/Opk2bGDJkCP369SMkJISNGzcWNk+Kjo7G09OT+Ph4SpcujaurK59//jmdOnUqMpZOpytc8kb8tyTp8ojUpnNKm4EWBQX9Wq0KoEGFu8YcN405NirDx37/FUXhupLLWW0mKUoeeSiYoqaS2pRqmlKUUj15CWtuJiQcg1uX9J8bmoJVJXCoC8aWTzzcM/X3mzj5+fmF80kLvpaens706dNZunQpixcvpmfPnoXPiYyMZPjw4UyePJnGjRsXGbsgSBVCCCFEUQ/LsErAKsRL5kFZndmzZ/PNN9/w0Ucf4ebmxtq1azE1NWXSpEnY2tpy5MgRqlatSlRUFM2aNaN37958+umnnD17llq1ahWOU/D7LoHqf1+OouOGkkvunSVvjFRqbFVGGP+LtVtfFX8PJs+dO8ewYcNwd3enc+fONGrUqMjv58GDB/niiy9Yu3YtVlZW5ObmYmRkxO3bt7l48SKurq5FxpabREIIIcTDScAqRAkTHh5O2bJlcXZ25tixY9SrV48LFy7g4uJCWFgYwcHBzJw5k3Xr1nH06FGqVq3K5cuXcXFxYdu2bRw4cEAujoX4F3bt2kV2djbXrl0jNzeXUqVKsWzZMkJDQ7G0tCwMbJOTk5k9eza3b99mwYIFUl4vhBBCPAWZwyrESygnJwdj46JNar7//nuWLFmChYUFarWaTz75BG9vb/z8/Pj5558ZM2YMVatWpXbt2qxcuZKpU6dy+PBhNm3axCeffML58+efqFusEK+iB1UbhISEsHPnTsLDw8nIyMDR0ZHNmzcDsH37doKCgvj4448LA1Zra2saN25MWFgYeXl5GBo+eg6vEEIIIZ6c1IYJUQxSUlKYNWsWO3fuBPQXzwsXLiQvL4/Y2Fjmz5/Pli1buHr1KtOnT0dRFGbMmMGCBQsAcHR0xMPDgzNnzhATE4Ovry8jRoxgyZIljBs3jurVq0umR4iH0Gq195XnpqSkMHjwYPLy8ggLC2PUqFGUK1eOyMhIAPr168fmzZvRarUYGBgUlvg2bdqUadOmSbAqhBBCPCcSsArxAp07dw7QdxkNCwtDURRCQ0Pp0aMHhw4dIjk5mZEjR3L+/Hk8PDwICAjg5s2b/PbbbwQGBmJhYcHChQsBCAgIYMWKFTg7O6PVajl9+nThfNaePXsW52EK8VLTaDSkp6fz0UcfMXv2bI4fP461tTU9evTgxIkTALRs2RKdTkdERAS5ubk0btwYY2Njli1bBtzt2l0wn7Vg2SkhhBBCPFsSsArxggwePJhu3bpx4sQJjIyMqFatGsuXL+f8+fPs27eP7777Djs7OwwNDdm8eTOLFy8mKCiIcuXKsXjxYgBmzpxJXl4eADY2NlhZWaHT6dBoNDRr1ozx48ffV2IsxKtKq9WycOFCunXrxvbt20lLSwP0pb8BAQGYmZlRuXJlOnfujFarZcSIEaSlpXHixAnKli2Lr68v+/fvJyoqCoDg4GB69+79wNeSzr9CCCHE8yFnWCGek9zcXHbv3k18fDwAdevWJSEhgVWrVgHw7rvvkpiYiJeXF05OToWZnaSkJIyNjfnzzz8LyxHLlClDfHw87du3Z8yYMUVeRy6UhdDLSYMrh+HSHzB9zHIc7Z2IjYnD09OTFStWsGLFCgB8fHzYtm0bHTp04JdffiE+Pp6lS5fi4OBA69atC6sY2rRpg1qtLiz3rVWrFpaWxbzmjhBCCPGKkaZLQjxj2fkK55J1nItJYMXCYKpWqcyiWZ/TtGlTIiIiSE5OZuPGjVhZWREQEMCtW7do27Yta9eupU6dOlhZWdG/f3+mTJnCpk2bGDduHB07diwcX5amEaKo9AS4sBXi94NOq19z9mqUEbkZ0FyZS9V6YG0VzLUbV8jJyaFcuXLs27eP8ePHM336dN5++20++OADBg4cSK9evWjcuDFXrlyhYsWKLFmypLgPTwghhHilScAqxDOSq1XYHpNPWLyWXC2gssW29Uh+nDGATOfGdKhXmdq1a2Nvb09ycjK7du3CxMSE0qVL07BhQ2bMmMGFCxeoWrUqDRs2ZP369djb2xeOX9CZVAJVIe5KPAuH5oOigLk9qO+c1d50fpcfTk/hZMIustY14VxKNuV8TAtL5mNiYqhduzaBgYFERUURHR3Nhg0b6Nu3L6GhoTg6Ohb+zv19fVYhhBBCvDgSsArxDORqFb46lcfZJB3lzcBIowJUVPCsQ3r7XhxcN4vbykQyDkWyas6bGBkZsW3bNnbv3k2jRo3o1KkTjo6OHDp0iKpVqwIUBqtarRaNRiMXzEL8TWo8HJwHxpZgbHH/1zv7jWDl3rG09hrAqp2f4ny6JtVrVqNzl45YW1uTk5PDoEGDyMrKYuzYsfj7+wPg4eEB3C23L4m/ewV/N4QQQoiSruSdhYV4Cf1+KZ+/knQ4WRQEq3c1emcENnaO5FwK59jRcL4NPUCZMmXo1asXqamprF69GpVKxdy5cx/Y3VcuOoV4sDMb9RnVBwWrAN3rjyExNZ5jF7ez/oMLBJTvzdKFK3jvvfeoU6cOw4cPx8DAgPHjxzNz5kxq1qz5Yg/gOSjoVlzwd+P8+fNkZmYCyPrMQgghSiQJWIV4Sll5Cnsuaylv9vB5pa+9ORhtXg65aUn8uGUHiqLQoUMHfvzxR5YvXw6Aubk5IBeVQjyOjOtw/RSYlXv0dm28B6JSqbEwLU27eoMY+foarl69SmRkJL6+vixduhR3d3eg5C5Nc+/fjIJs8OHDh2nQoAE9evSgX79+hWvPCiGEECWNBKxCPKUzSVrydWCoefjFYIWaXni3fIvbqUkk37zJX1f1y2t06NABBweHIhecclEp/klJDayepYSjoFLrPx6ld+Ak9v31P+KTzmNqA1wtz9rlP9GiRYvCbQq+nyWt9FdRlPsC0by8PEaPHs3MmTMZM2YMR44cITExkc8++wzQlwoLIYQQJUnJOjsL8RK6nqmgUT06K6rT6bCydWRo8A5aTVxNnpF54dcURZEgVTyWkhpYPQ9ZiaAx+uftjAxMeNN/JFeTo1GpQKUBXbZhkaC/pH0/C9aFValUaDQasrKyWLp0KeHh4RgaGuLv78+NGzcoU6YMAF988QVLly4FZIqBEEKIkqdknaWFeAk9TqxZcEHsWL0OKrUByj0XyxKsisdV8HMUGhrKihUrSE5OLuY9KkZPcPYa2nwOftVaAfolb1Sqkhekgj47+ttvv3HgwIHCgHvr1q34+fmxZ88eli5dysCBA+natSuVKlUiKSmJ3Nxc/Pz8cHFxYd68eYBk6IUQQpQs0iVYiKdkb6ZCq3u8oFNRFHQKlDOTLId4NEVRUBSlSGAVERHBsmXLiImJwcTEhG3btvHNN99gampajHtaPMzsID/n8bfXKTpUihp0YGz1/PbrWVMUhdjYWGxsbLC0tKRly5bk5+dz9OhRfHx8iIiIYPTo0fTt25esrCxq1KjBiRMnaNGiBfv27cPV1ZVq1aoxduxYPvvsMz788MMSGawLIYR4dclZS4inVKuMBiMD/dI2/yQ1FypZqbA3k6yqeDidTodKpSoSWFy6dIklS5Zw6tQptm3bxubNm8nIyGDnzp2v5LxEx3r6TKnymMlCtUpN1k2wcwdT6+e7b89CYmIip0+fRlEUdu/ezfbt24mOjubPP//kq6++YuHChSiKwv79+zEw0N97LlWqFEOGDCEoKIgOHTpw4cIFjh8/jqIodOnShePHjxfzUQkhhBBPTgJWIZ6SiYGKphU1XMtQHtnhN0+nkJoNzStppAxY3OfkyZMEBQWRlpaGWq0mKyuLzz//nL59+7Jnzx4qV65Mq1atMDMzIzo6GoAmTZqwbds2kpKSinnvXzxTG7D3hoxrj7e9okBuBrg0f7779bRu3LgBwO7du7l06RI6nY7w8HA++OADunTpgqWlJV5eXlhaWnLkyBH69OnDggULCp/v4uKCk5MTlpaWtGnTBjc3N1QqVWEZ8Kt4c0MIIUTJJgGrEM/AG5UM8LE3IC5NISuvaNCqKAppOQpXM6B9VQPcykklvrirIJC4cOECwcHBWFpaEhkZSfPmzcnLy6N///5MnTqVHTt2EBgYiIeHB6GhoQD07NmTM2fOcOTIkVdyOaTaXUFtCLdTHr2dokBqLFR4DcrVejH79m/ExcXh4OCAVqula9eu2NvbExsbS40aNahSpQr/93//h7u7O66urlSvXp3vv/+ebt26YWpqyqRJk5g5cyYzZ87E09MTgIEDB1Krlv6AC7L10nRJCCFESSMBqxDPgEat4p1aBnStYUi+Di6nK3c/MsDMUMVADwPeqCQXi686RVHIy8tj9erVwN1AokmTJpibmxMbG0ulSpXYtGkTffr0YenSpVy5coXp06dTtmxZ6tSpw+nTp0lISMDe3p6mTZtSqlSpVzJrb24HAeP0ZcGpcffPaVUUyL4FKRfBvi549fvnZXCKQ8FNi4oVKxIYGMicOXMA+Omnn3j//fd5//33GThwIPv27SM5ORkrKyu8vb3JyMjg8OHD/Prrr1SvXp3o6GhWr15Nx44dC8d+FW9kCCGEeDZelnPIS3jqFqJk0qhVNHIyYEp9Y97zMqJnbUN6uxoyup4R43yN8Chn8EoGFaIolUpFfn4+EyZMYPHixSQkJAD6Oapubm5kZ2djbm5OXl4egwYNomnTppw6dYpr166xf/9+mjRpQnp6Ovv37wfgo48+onHjxsV5SMWqdCVo/AlUbwc5qXArVh+8psbps6rGVuA7HHyGgYFxce/tg6nVam7dusXGjRupUaMGy5YtA2D06NFcv36dM2fOULduXXJycjh8+DAAlStXxsHBgR9++IHSpUvTq1cvgoOD8fLyKmzYBdKFXAghxJPT6XRFll0s7u7yqpclcn6UevXqKeHh4cW9G0II8dS0Wi0ajYa9e/fy008/kZ6ezldffcWtW7d47bXX2LFjB46Ojnz33Xfs37+fxYsXc/nyZdq0aYONjQ1//PEHFy9exMXFpbgP5aWTnwMp0ZCbCWoDKFUGLJ0eb+mpF6Wgoda9gWRmZiYtW7bEzc2NBg0aMHLkSKZNm8agQYOYMGECN2/eZOXKlSxcuJCwsDCsrKwIDAzEx8eHypUrU6pUqSLjSxdgIYQQz0J6ejqzZs2iZcuWBAQEPPfXU6lURxVFqff3x2UynRBCPEcFAWqBgs8bNmyIq6srPj4+rFy5kn79+uHn58dvv/3G4MGDcXJyIj4+niFDhnDx4kVGjx5NlSpVACRYfQgDYyhXu7j34tEKgsmoqCgsLCwoX748UVFRuLi4FGZWzczMGDlyJIMGDWLIkCF07NiRixcv8sEHH3Dr1i0MDQ3p2bNnYXfge++CS7AqhBDiSf39WgX0VT7JyclUqVIFb2/vYtozPcmwCiHEM/agcsysrKzCTJiiKOh0OjQaDb///jshISFkZ2dTsWJFatasyZtvvgnAuXPn2LhxI82bN8fPz+/FH4h4an/PeEZHR/PRRx9x8eJFWrZsiampKe+++y7+/v7ExcWRl5eHoaEhjo6OLFu2jPbt29OrVy9cXFz45JNPivFIhBBCvDQSEyE2Fi5e1P+bmwsGBlC+PFSrBhUqgNOTlxhFRkZiZmaGs7Mz06ZNY86cOVy9evWFrff+sAyrBKxCCPGMPKjcc926dYwdO5bOnTvTqlUr2rZte9+dzJiYGLp3787Ro0f58ssvGTBggJR2lnAPe/++++47TE1N6dixI7NmzWLatGmkpqZSr149+vXrx7Bhw0hNTaVVq1bcvHmT8+fPk56ejoWFRZGx//5zJoQQ4j9OUSAyEn7/Xf8v6IPUUqVAowGdDrKzIedOB0JHR2jVCnx8wMioyFB/P0edPHmS4cOHY2RkRO3atWnevDlt27bF3Nyc8PBwatas+UKuSyRgFUKIF+TGjRusWLECOzs7srOzadasGadOnWLOnDkcOHCgyB/8ghNAWFgYU6dO5e2332bQoEHFuPfiWVq7di0nTpxg6NChVKlShTZt2mBra0t0dDQODg6MHDkSf39/du3axbx587CxsSEuLo5hw4bh6OhYZM7QvaW/QgghXiFJSfDNNxARARYWUKYMPCp4VBRIS4PkZHBwgEGDUJydURTlvqBTq9Uyb948/Pz8aNSoES1btsTQ0JBff/2V0aNHc+nSJX7++ecHlg0/axKwCiHEM/b3P96pqanMnj2bhIQETE1N2bdvH56enqxZswaATp060bRpU4YPH/7AP/ySVX35KApoc/T/Ghg//rI4wcHBxMXFceXKFWxsbLh48SKDBg0iKSmJDz74gPj4+MIS8YMHD+Ln50dmZibffvstPj4+j5wvJD8nQgjxCjl9GhYv1mdQ7e2fuMz34sWLuKjV0L07tGwJKhWLFi0iOzubrl274uzsTJ8+fbh58ya3bt2iRo0afPzxx7i4uJCeno6dnR3nzp3Dycnpud84lYBVCCGekb//wc7Pz8fAwIDLly9TrVo15s+fz5AhQ1i6dCnR0dEMGTKEatWqsXnzZmbMmMGBAweKce/FP1F0kHQeLm6HhAh9sKpSAQqUqQlVW4Ktq74TccF7X7C+7o4dO2jdujXdunXj0KFDxMTEoFarCQkJYerUqezYsYM6deqwZMkSKlasyPLly4mLi2PWrFnUqFHj7j48YB703x+7d160EEKI/6CTJ2H+fH1G1dz8iZ56NjGRxYcPY25kxMzXX0cbE0OoiwtfnTlD2bJlUalUJCQkMGLECPbs2cOxY8dYuXIldnZ2ABw7dgxvb2/effddXn/9dfr06fM8jrCIhwWscotWCCEeQ2pqauHnBQHD1q1bef3115k6dSr79++nQoUKvPvuu/zxxx8AtGvXjsTERE6ePIlWq6V169bk5uaydevWF77/BY2exMMpCsQfgO3jYN8MuHEGLCvo13q1qgiWFSElTsukQYvo0+AzTv6SiVplQFZWFiqVip07dzJ79mwAPvvsM65fv14YZDZt2hQbGxsuX77M4sWLCQsLY8CAAVhaWrJ69er7gtV756jeG6iqVCouX77MyJEjWbRoETkFc5WEEEL8t1y5AkFBULbsEwerAL+cO0dpExM+bdyYo4mJ5Ds5sX7dOi6dPs3y5cv58ssvee211/jzzz+xt7enTJkyhIWFcf78ebp3787cuXPJzs5m1apVLyRYfRQJWIUQ4h5ZSXD9FCQcg8QzsHf3Qbp27UqnTp3YuHEjt27dAmDBggUEBQUxf/58qlSpQv/+/VEUhVGjRnH27FliYmJwcnLCzc2NHTt2EBMTg4GBAVu2bKFVq1Yv7HjuXfxbrVZL0PoQig7O/ABH9CvLUNoZzO3ulgDfzs1k1R8f89GvjYjK2EMj185E/2TGV+P2Uf+1+gQHB6NWq/H09ASgRo0a+Pr6MnHiRECfibWxscHMzIw2bdowc+ZM9u/fz7Rp07CxsSnyvhQEqlqttsj/AVauXEndunVxdXVl/PjxGBsbP+fvjBBCiBcuPx9WrNA3SzIz+8fNs/LyOJaQQE5+fuFjjhYWHLpyhZPXr9Px++8xNjGhu5cXZbOyOPfnnwC4ubkRExND586d6dq1K9u3b+edd97B19eXNWvWYGJigkqlKryWKC4SsAohBPoS0D+DYNtoODQfDi+Gjm260bltd5pW6c/bnfvyyy+/8N133wHQp08ffvjhB8LCwli2bBmJiYl8/fXXVKtWjcDAQObOnQtA+/btqVWrFuXKlQMoLLV5UdRqNSqVivT0dD788EPatWvHwYMHyb/npPaqUxT4639w7ld9oGr0gBvZV5KjWL93OnN6bWdq1x+o5uSKaYXblL3ZgLFt1nDkSDjDhg0rEkDOmDGDWbNmsWDBAnr16kVKSgplypQp/LpGo0Gr1T6wCUbB1wE2b97M+vXrAXjrrbdISUnB2dkZgLy8vGf3jRBCCPFy2L0bLl0CW9vH2jwuNZX/27mT5UePFj7WoGJFFEXB1swMb3t7vj99mtdr1MC9dGl+nDYNAF9fX06dOoWpqSlt2rRh0aJFhIeHM3r0aAwNDQtvphZcSxQXg2J7ZSGEeAkoCkSFwJmNYFBKX/pZkFVzq1qP1Pyr2Ce2RJuUTRX7q4WBhbW1NatWrSIsLIzDhw+zbt06pk+fTp8+fejVqxf9+vUjKyuLmjVrUrNmzRd2PPc2cyrIrL7//vuoVCqcnJxwcnJixYoVXL9+nY4dO76w/XqZXT8JZ/+nL/1VP6QBYtXynlQoUw2N2oBbmYks3zEeYwNTejWaTKl4TwZ3+IIDBxqxa9cuxowZw7hx4wgICKBZs2YcOnSIGTNmUL169fvGvfe9Klibt0BUVBT9+/fHycmJ8uXLc/z48cKfsW+//ZZmzZpJ8yUhhPivyc+H336DJ7jBXbNsWaY1aUKPn3/GuXRp2teowbWMDCpaWWFtakp3V1e+PHqU7m5ueFWpwpjQUAwmTWJLWBj169fH0NAQABMTE0B/LaFWq1+ac8zLsRdCCFFMLv0BpzeARYWiJaAAbweMIz7pL9JKncfMwoTDWy6hytaX5uTn55OQkEDt2rUBMDIy4q+//mLbtm34+vry559/FktDnIKAJy4ujszMTEA///b3339n7NixDBs2DH9/f7Zu3Vqs5T0vC0WB87+CaWl9E6VHGd4yiA6zyjD1h66Us6zAwDe+oIyFHWblFRL3lsHNzZ0ZM2ZgamrKoEGDOHnyJOPHjycsLKwwWC0o872XVqtFpVKh0Wi4ffs2cXFxAERERNC/f3/Wr1/PtWvX2LNnD5cuXWLKlCn88ssvZGRkoNFo5H0UQoj/krNnITVVv77qE6jr4MBHDRrw9YkT/BAZSZ3y5QmLjSVfp6O7qys6ReH3CxdoV7Mmbzo7c+nwYYKCgpg7d25hoFpAo9G8VMuoScAqhHhl5WbC6e/0jXU0hg/epo33AKZufJMvQrtyKGYTQfOWsHjxYgwMDKhRowaxsbEEBAQQEhLCt99+yxtvvAGA2WPMOXkW/h4AHT9+nICAAHr16sWwYcM4d+4cCxYsIC4ujqysLIyNjfHw8AAoluZPL5v0K5AUBSbW/7ytT5XmGGqMmNh5Pf0af4qZsSUARmYqzlw6RkLsTZo0acKkSZMIDg7Gw8ODJk2aYGtrS0REBHD3hsKNGzfo0aMHOTk5aDQaUlNTGTt2LK6urvz2229otVqSk5OZOHEiPj4+uLi4sH37dqpXr06FChXw8/Pjww8/fG7fFyGEEMXk+HH93NV/oY+nJ709PJhz8CB7YmNpULEi++Li0KjVtKtenbkHD2JlYoJ3pUrkX7uGu7s78OCbqS8TCViFEK+sq+Ggy9Ovr/kwfV//hKspF/Go2IhN427QrsoENv2wmf79++Pt7c2ECRMYOnQoa9as4a233nrui2oXKDi5FKUHnqcAAB5+SURBVLxeXl4eOp2OtWvXMmDAAMLCwnB0dGTevHkYGhrSrVs3xo8fD0DVqlWpVKkSP//88wvZ15dZTJj+ZsXj3EhWqVQMazGfb8I+LXzsemoce87+jKWlBZXN/AvLem1tbQvnlx4/fhwvLy/g7vtma2vLokWLCsf56aefuHXrFhEREQwbNgyNRoOlpSUNGzZk1apVfPbZZ1haWvL777+TmZnJvHnzCkvNX6a74EIIIZ7SuXNgYfGvn96hZk3e9/Xl4127OBgfj92dG+jDfHz45s5UoJoODtgqCudPngR4Ydcu/5bMYRVCvLIu7QBTm0dvY2RgTFO3t4lJjESlAn/nDrRs5M2vpxei1Wpxc3PDzc3txezwPe5tyLN27VoGDRpEs2bNCAsLo2nTpgAMHjyYVatWsX37dj755BM8PDwICgqibNmydOrUSdbwBBIjHy+7WuAN93dYvn0crb37szViFZHxB2hfbyj1vTpga/Q5KhUUTPkpmBN0b0B570VBQkICnTt35q+//iI+Ph5TU1OCgoKwt7fHyMgIFxcX3N3d+eCDDxg+fDgrVqwgIyODr7/+mlq1alGrVq1n8j0QQgjxksjPh6tXoUKFpxqmp4cHRhoNI7ZuRXdn2kgpQ0NK3Tkv+VWoQH0A6yc4ARYjybAKIV5ZWclgYPrP2/Vr8jm/HQ0mNSsJjTFY4cScOXOoVq3a899JeGA7+W3btuHl5cW+ffs4e/Ysy5YtIzU1lW7dunH8+HEAnJ2diY2NJSMjg8qVK+Pp6cmWLVsAqFWrFpUqVXoh+/8yy8v857mr99KoDXjTfyQffduGyrZuLB90lA4+QzEw0KDoIC+76LJBD5pf2qZNG86dO4erqyvOzs5s3LiR0aNH4+DggKmpKQkJCaxevZrw8HBGjBhB7969CQkJoXfv3uzduxcXF5enPWwhhBAvo9xcfXOFp2h2VHDe6Vq7NldHj+Y1J6f7ttGo1frSohKylrdkWIUQryyVGniMfjU25nYMaT6HPG0Oap2CSvPiSjDvXfIkPz8fAwP9n+3jx4/Trl07Pv30U95991369u1LVFQUXl5ezJgxA29vb/z8/EhMTMT2Tlv8P/74o/D54o5/cU3wVsA43mkwofD/Wp0WtUoNqNAYqAsvFlQqVWF29eLFi1hbWxd+TJkyhQ0bNjBgwADmz59P9+7dmTDh7pjXrl3D0tISc3Nz+vbtS9++fZ/mKIUQQpQUTznNo+C8o1KpUAH5Oh0GDwuAS8iUEsmwCiFeWRYOkJvxeNt2e+1Dylo4oM1VYeHwfPdr/fr1fP/994D+hHP27Fn69evH0KFDCQkJAeDq1atUq1aNnJwcatWqhbu7O2vWrKFZs2b069ePtWvXUr9+fd544w2aNWsGgIGBQeGaakLP2AK0uU/2HPWdVtJanX4tW41ag6JVkZQTh9pAKRKobt68mZkzZzJ16lTOnj0LwLx58wgJCeHmzZt07doVExMTfvzxRwA+//xz3NzcSEtLo3Hjxs/oKIUQQpQIxsag0cAzbIL00GAVnrgTcXGRgFUI8cpyaQY5aY+/vU6rvxnpUPfZ70t6ejo5d0pzoqKi+PLLLwHYvXs3gwYNokmTJgwYMICPP/6YqKgoqlevzsmTJ0lKSgKgSZMmbNq0iejoaHr06MGyZcs4e/YsH374YZF5ky/LmmovCwcfyE75d8/V3Kkl3n5yPUNX+PND9AR2794NwIYNG5g8eTI7d+6ka9eulCpVisjISFJTU7G1taVFixZ8/PHHALz11ltMnz4d0Jdqb9q0ibVr11K5cuWnPj4hhBAliEYDFSvCnWXpnpuCm9dPsNZrcZIrFyHEK6u8BxiZQ076422fngAV/MGk9LPdjx07duDs7MzmzZsB6N+/P2lpady4cQNvb29CQ0Oxs7Nj3Lhx5ObmMnnyZPr378/169eZMWMGX375Jbt376Z8+fJs27YNAIs7HQa1Wq2s0/kITvX104WUf5F41ury+XL7OEKPr6GH5zRWfrUSR0dHAIKCgggPD+fzzz/HxcWFhg0bcuLECaKjowFo27YtwcHB5ObmFpb85ufn06VLlxc2N1oIIcRLqGZNSHuCu+n/RkYGODqC4UPW9HvJSMAqhHhlaYyg7hC4naRfk/VRMq6DiSXU7vpsXjsyMpL0dH2k7OXlRdWqVQkODiY8PBxLS0u8vb2JiIjA0tKS8+fPM3v2bL744gv27t3Ln3/+SWxsLEuXLsXR0ZFjx44xZcoU+vXrR3JyMnB3DsvLtvj3y6ZUGbD3gszEJ39ucsY1TsXtZVKbX6jv3RRHt1JUr14dgPfeew8DAwMyMvQ15x06dECr1bJp0ya2bdtGQkICbm5uHDt2DCMjI0aMGCHzi4UQQoC3t75b8PO82ZySAoGBz2/8Z0wCViHEK83ODfxGQk4qpMZB3u27X1MUyL4Ft2LAxAoaTADTZ9QBfuDAgSxcuJCEhAS0Wi29evXC39+fRYsWoSgKOp2ucL7prl278PHxoX79+pw5cwa1Ws3EiRMxNzdn/Pjx/N///R9Llixh8eLFtG/f/tns4CukelvIz9Z/PEzuA754JTmaija1yEnWUKOjDpWKwvesS5cuZGZmcvz4cbRaLebm5gwbNgxFURg/fnxhSbe/v//zOiwhhBAlkYsLODg8vyxrfr6+C7Gv7/MZ/zmQgFUI8cqz94Q3ZkDNzpCXBanxkHZZ/6+RhT4L+/pUMLN9+tfS3mmkMHfuXFJSUli5ciW2tracPn2aAQMGYGVlxZ49e4iPjy9cnqZy5cpER0fToUMHPv30UxYsWMC8efMKx/zyyy8xNDTk2LFjuLu7P/1OvmKsXaDuQEi/+uCg9fCF31n9xxSuJOvLebU6/XuoaFWcjv0Tj27GVPDVn04LstkmJiZ06tSJn3/+mbQ7Fx3u7u58+umnRERE0Llz5xdwZEIIIUoclQo6dYKkpOeTZb1yBd54A+5MHSoJpP5ICCEAUxuo0RaqttA34dHmgYGJ/vGnqajVarVFmh4VfP7aa69hZGTEgAEDaNOmDWXLlmXbtm1MnDiRkJAQsrOzCQ0N5aOPPqJTp054eHgQEhLC22+/TdmyZYuMPWPGjKc6dqGfywpwdAUYGEOpcqCo8tGoDbC1qkhO/m3OJxzF0aYKapWGrJvgrGqEkYWOg8lrqKnqi6LoOwRnZ2ezadMm+vbtS2BgIPHx8VjfWZxdml4JIYT4R/XqgY8PnDgBFSo8u3Fv3QIbG+jY8dmN+QLImVMIIe6hMdRnUi0d9fMb/22wWtDoqCBAzc/Pv+9rdevWpX///kyaNAkfHx+2b9+OnZ0d7du3JzU1lfPnz3Pu3DkAqlSpwvvvv0/ZsmXR6XQoilIkEBZPz6k+BE4GOw99hj3jsgE5aXA29hi3b2dyLi6C+KgkUmOhlEMuDSbA57Mms3r1Kn755RdUKhXp6enMnDmT/fv3Y25uzpYtW/Dw8CjuQxNCCFGSqFTQs6d+2Zk7qwE8tdu39QHr4MFgavpsxnxBVCWhe2S9evWU8PDw4t4NIYT4RzqdrkgW7dy5cwwbNgx3d3c6d+5Mo0aN7ttm8ODBZGdnk5WVxbx583BycmLLli2kp6fz1ltvFRm/IIsnnq2CuacF78vtZDj0SyyjPumDg2V1DDXGnLm2j9HvTqfniFaY2d19H3744QeCg4OxsbEhIiKCwMBAJk+ejJOTU7EdjxBCiP+Ay5fhiy/0n5cr9+/HyciAxEQYPlyfuX1JqVSqo4qi1LvvcQlYhRDi2du1axfZ2dlcu3aN3NxcSpUqxbJlywgNDcXS0hJFUVAUBbVaTVRUFFOnTmXDhg3ExMRIoPOUCk5rjxPXF5wDC4LPgs7NFhYWhIaGMnPmTHbt2gXAxIkTMTExYfjw4VhbWxcp97516xaRkZFUr16dck9zUSGEEELc6+pVWLQIEhL05cFP0lFeUfRzVg0NYdgweMn7XDwsYJU5rEII8S/9PdgBCAkJYefOnYSHh5ORkYGjo2Ph+qrbt28nKCiIjz/+GJ1OVxjsVKtWjblz5zJ06NAiwapkUx9P5g24ehQSz8Cti/olilRqKFUWbKrrS3zL19HPTf27gu9vQkICkyZN4siRI7i5ubF+/XosLS1xdHQkLi6OihUr4uPjw+rVqzlz5gwBAQFFsuSlS5cmICDgRR2yEEKIV4WDA0ydClu2wG+/6e/G2tqC8QNOagXy8+HGDcjN1c+H7dkTSj/jReRfIJnDKoQQ/4JWq0WlUhUJKFNSUhg8eDB5eXmEhYUxatQoypUrR2RkJAD9+vVj8+bN9zViAihfvjwNGjQo8pgEq4+WcQ0OLYDt4yFyI6TGgrEVWFUCyzs9KhKOQvgy+P0DOP8b5N7WFj5fURS0Wi3r1q1j1KhRuLi4cOLECWJjY1mwYAHGxsbY29vzxx9/AODr60tkZCQhISFkZWXJ+yOEEOLFMDaGzp1hxgxo2xZSUyEuDmJj9WXDV6/qM6mxsfrHExLA3x+mTIH33ivRwSpIhlUIIf4VjUZDeno606dPx8bGhmbNmuHp6UmPHj04ePAgAC1btmTnzp1ERERQrVo1GjdujLGxMcuWLWP48OHFfAQll6LApT/g1HpQG4CVkz6jWoQKDEvpPwDycyDyR7h8SEO9IXD84h6qVq2Kg4MDVlZWxMTE0KFDBwCmTZvGxIkTeeuttwgICGDKlCnExMQQHh5O9+7d6dGjB6VKlXqxBy2EEELY2uqXvGnbFq5f1weqV69CTo6+VNjOTp+RtbcvcY2VHkUCViGEeAStVsvixYvZv38/AwcOxM/PD0tLS0JCQpgwYQLdunWjcuXKdO7cmaioKEaMGEFoaCgnTpygTp06+Pr6sn//fry8vHB1dSU4OBhHR8fiPqwSS1HgzI9wbrM+i/qgMt8HMTAGa2fYHR7Ce/U+x65KKQJe96NKlSr06dOHsLAwkpOTyc3NJTAwECsrK/73v/8xZMgQbGxs2LVrF++//z7Nmzd/rscnhBBC/CNDQ/181me55M1LTEqChRACUHSQdF5fPho6GkLeh5Etl2NfzokLZ+Pw9PRkxYoVrFixAgAfHx+2bdtGhw4d+OWXX4iPj2fp0qU4ODjQunVrFi5cCECbNm1Qq9UYGhoCUKtWLSwtLYvtOEu6S7v0wWpp50cHqxM3dODPqK0AaHX6MuDMnDRua64zstlK3qu9ifNnolm1ahW3bt2iYcOGREVFcfz4cQD69OnDypUrycvLo2HDhkyZMkWCVSGEEKIYSMAqhHjlpVyCnRNh73S4dkK/FquxBWgwIj8bmmbPpan5/xHY4A3S0tLIycmhXLlyXLhwgSFDhjBgwAB++eUXgoKCyM7OplevXmzdupUrV65QsWJFlixZQvXq1Yv7MEu89AQ49a0+s6r+hyVoazn4snbv58DducBmxpY0dX+HhOxIxv5UH8MUJ2rWrEVwcDDNmzdHq9USFhYGQLdu3fj1118LbzQIIYQQonhIwCqEeKXdPAd7p0Fepj5rZ14eDExAYwTt/N/F0NCAaN0urp+Es79mY6AyxfhOZ76YmBhq165NYGAg1apVIzo6mg0bNlCzZk1CQ0NxdHQsXN+z4F/x751cp7+Z8DhlwD0bTST+5jnOJxxDrVIXZll1ipZTcfsY0zGYrhVnY5BjxY8//kh+fj4NGjSgdu3a6HQ6FEXB3t7+OR+REEIIIf6JzGEVQryysm7CoflgbKn/eJDOfiNYvmMsbbwG8M3eT3E+X5NartXo1Lkj1tbW5OTkMGjQILKyshg7diz+/v4AeHh4ABQufXLvEijiyaVfhcRIfQfgx9XWeyCrdn3MjB4hgH7dWxPDUhy9uJ26Lm+g1YWTcCmTGjVqEB0dTffu3Z/b/gshhBDi33luV1AqlWqVSqW6oVKpTt/z2FSVSnVFpVIdv/PR+nm9vhBC/JNLu0Gb9/BgFaB7/TEkpsZz9OJ21n9wgQaOvVkatIL33nuPOnXqMHz4cAwMDBg/fjwzZ86kZs2aL2z/XyVXDoNKo19+7nH1eX0Kp+P3cyX5Ahq1QWFpcGe/ERyK2sKcHe9Q27wFwfO+xdPT8zntuRBCCCGexvO85b8GaPmAx+criuJ55yPkOb6+EEI8VH42XNoBZrb/vG0b74GoVGosTEvT2mMQY1uv4erVq0RGRuLr68vSpUtxd3cHpPT3ebkR+egbCw9iZGDC667d+fHQAgA2HV7M8FUBmBiaMaTZbNa+fx6/Sh1Ju/wcdlgIIYQQz8RzKwlWFGWPSqVyfl7jCyHE00i+APm5jzcfsnfgJFpNNyc+6TwVbKuTFlWe79f/hKnF3YY8Op0OtVotpb/PgaKDW5fA/F9MKe3X+DPenGvPgXObcSpbg17/3979x/pV13ccf75aipSCtECHlB+2VmBzOMtWEMKKhSz4Y4QfbiokU7YRcE4XEI2OxQDLkg1RjP8YncxluCGOxBENMRtuAyoGQcBCQfw1KNIVKQxoqY5f9773xzmVu/L9Xtr6vfec9j4fyc33ez/f8z33077yzv2+7zmfc1Z8jDce9tZfvD5rN3jyAVi0fIQTliRJI9PFGtYPJHkPcAfwoap6soM5SJrhnv85bOvZpbvvtge/f+wFrH/ivzhkv/Zqvy/M+UWTCq5RnUpjz8P42MtfGXiQffc6gAtP+VuWvuoN/NpBx7zk9dlz4JlNI5ikJEmaEtPdsH4W+Cug2scrgD8etGGS84DzAA499NDpmp+kGSLb2V++7+RPTnhz836b1OmxPetWBznlt86d9HVjlCSpv6b113RVPVpVY1U1DlwJvPTP3S9u+/mqWl5VyxcuXDh9k5Q0I+wxn+ZPZ9thvMYZe645Krf7vCmZlgaYNQfm7Aljz41+388/s23rmCVJUjemtWFNMnEF0hnAvcO2laSptOA1MHd/ePbpbX/PrMxi86Ow5KRm7aOmRwL7Lt2+rLZ532zfrXIkSdL0msrb2lwD3AockWRdknOAy5OsSXIPcCLwwan6+ZI0mcyCw97W3It1W42PQY3BoSumbl4a7FXL4PnNo93n+FjzuI+rTiRJ6q2pvErwWQOGvzBVP0+SttfBx8LaG+Hp9bD3osm3rXF46iF47Vtg7x24Wq1+OYuWw5qrYfyF0R3d/tkGOOiNsMc+o9mfJEkaPS81IWnGmjMXjr0A9lwIT60dvkbyuc3NbVVefQIc+a5pnaJar3glLD4RNq0fzf7GX4CxZ2HpyaPZnyRJmhquwpI0o83dF1ZcBD+8Hh68sWliZu8OmQ3j7e1U9twPjjqnaVi39+rCGp1fPQPW3wHPbPzlj4puWtecEr5gyWjmJkmSpoYNq6QZb/e94Mgz4YjT4KerYeNP4IX/hd33hoWvg/2PsFHtg93nwdF/Crf8TXNa8I5eqXnTOpi/BI44dbTzkyRJo2fDKkmtOXPhkOOaL/XTfofDsRfCtz8Nz/8c5m3HXc/Gx2DTw02zetwHYbc9pm6ekiRpNDxmIEnaqRzwelh5SXM695MPvPztbmocNj/aHDlfejIc/5FmTawkSeo/j7BKknY6+xwKb7oYHv4W/PDrsPEhKJqjprPnQFVzWveWW9cceFSzZnXf13Y6bUmStJ1sWCVJO6XZc2DxyuZiWE+thY3r4MkfwbObIYG9FjUXVZq/uLlwliRJ2vnYsEqSdmqZBQte03wtPqHr2UiSpFFyDaskSZIkqZdsWCVJkiRJvWTDKkmSJEnqJRtWSZIkSVIv2bBKkiRJknrJhlWSJEmS1Es2rJIkSZKkXrJhlSRJkiT1kg2rJEmSJKmXbFglSZIkSb1kwypJkiRJ6iUbVkmSJElSL9mwSpIkSZJ6yYZVkiRJktRLNqySJEmSpF6yYZUkSZIk9ZINqyRJkiSpl2xYJUmSJEm9ZMMqSZIkSeolG1ZJkiRJUi/ZsEqSJEmSesmGVZIkSZLUS6mqrufwspI8BjzU8TT2Bx7veA76/8ykn8ylf8ykf8ykn8ylf8ykf8ykn3aFXF5dVQu3HtwpGtY+SHJHVS3veh56kZn0k7n0j5n0j5n0k7n0j5n0j5n0066ci6cES5IkSZJ6yYZVkiRJktRLNqzb7vNdT0AvYSb9ZC79Yyb9Yyb9ZC79Yyb9Yyb9tMvm4hpWSZIkSVIveYRVkiRJktRLNqwDJFmbZE2S1UnuaMf2TfKNJD9qHxd0Pc9dXZK/T7Ihyb0TxobmkOSiJD9O8oMkb+5m1ru2IZlcmuS/23pZneRtE14zkymW5JAkNya5P8l9Sc5vx62VDk2Si/XSkSR7JLk9yd1tJn/ZjlsrHZkkE+ukY0lmJ/lukuvb762Tjg3IZMbUiacED5BkLbC8qh6fMHY58ERVXZbkz4EFVfXRruY4EyQ5AdgMfLGqjmzHBuaQ5HXANcAxwCLg34HDq2qso+nvkoZkcimwuao+udW2ZjINkhwIHFhVdyXZG7gTOB34Q6yVzkySyzuxXjqRJMC8qtqcZA5wC3A+8HaslU5MkslbsE46leRCYDnwyqo6xc9f3RuQyaXMkDrxCOu2Ow24qn1+Fc0HD02hqloFPLHV8LAcTgO+XFXPVtWDwI9pClUjNCSTYcxkGlTVI1V1V/v8aeB+4CCslU5Nkssw5jLFqrG5/XZO+1VYK52ZJJNhzGQaJDkY+F3g7yYMWycdGpLJMLtcJjasgxVwQ5I7k5zXjh1QVY9A80EE+JXOZjezDcvhIODhCdutY/IPhxqtDyS5J80pw1tOEzKTaZZkMXAUcBvWSm9slQtYL51pT6lbDWwAvlFV1krHhmQC1kmXPg18BBifMGaddGtQJjBD6sSGdbDjq+o3gbcC729Pg1S/ZcCY57tPj88CS4FlwCPAFe24mUyjJHsBXwEuqKpNk206YMxcpsiAXKyXDlXVWFUtAw4Gjkly5CSbm8k0GJKJddKRJKcAG6rqzm19y4AxMxmhSTKZMXViwzpAVa1vHzcA19EcRn+0XZO0ZW3Shu5mOKMNy2EdcMiE7Q4G1k/z3Gakqnq0/cAxDlzJi6edmMk0add+fQW4uqr+pR22Vjo2KBfrpR+q6ingJpq1ktZKD0zMxDrp1PHAqe31XL4MnJTkn7BOujQwk5lUJzasW0kyr71ABknmAScD9wJfA85uNzsb+Go3M5zxhuXwNeDMJK9IsgQ4DLi9g/nNOFt+gbXOoKkXMJNp0V605AvA/VX1qQkvWSsdGpaL9dKdJAuTzG+fzwV+B/g+1kpnhmVinXSnqi6qqoOrajFwJvCfVfUHWCedGZbJTKqT3bqeQA8dAFzXfNZgN+BLVfWvSb4DXJvkHOAnwDs6nOOMkOQaYCWwf5J1wCXAZQzIoaruS3It8D3gBeD9O/PV0PpqSCYrkyyjOd1kLfBeMJNpdDzwbmBNuw4M4C+wVro2LJezrJfOHAhclWQ2zR/sr62q65PcirXSlWGZ/KN10jv+Tumfy2dKnXhbG0mSJElSL3lKsCRJkiSpl2xYJUmSJEm9ZMMqSZIkSeolG1ZJkiRJUi/ZsEqSJEmSesmGVZKkHZBkcZJ7X35LSZK0o2xYJUmaZkm8D7okSdvAhlWSpB03O8mVSe5LckOSuUmWJfl2knuSXJdkAUCSm5L8dZKbgfOTvCPJvUnuTrKq3WZ2kk8k+U77/ve24yuTrGr3970kn0syq33trCRr2n19vB17Z5JPtc/PT/JA+3xpkls6+H+SJGmH2LBKkrTjDgM+U1W/DjwF/B7wReCjVfUbwBrgkgnbz6+qN1XVFcDFwJur6g3Aqe3r5wAbq+po4Gjg3CRL2teOAT4EvB5YCrw9ySLg48BJwDLg6CSnA6uAFe37VgD/k+Qg4LeBb476P0GSpKliwypJ0o57sKpWt8/vpGkk51fVze3YVcAJE7b/5wnPvwX8Q5Jzgdnt2MnAe5KsBm4D9qNpigFur6oHqmoMuIam+TwauKmqHquqF4CrgROq6qfAXkn2Bg4BvtTOYwU2rJKknYgNqyRJO+7ZCc/HgPkvs/3Ptjypqj8BPkbTUK5Osh8Q4M+qaln7taSqbtjylq32Ve32w9wK/BHwA5omdQVwHE2jLEnSTsGGVZKk0dkIPJlky+m47wZuHrRhkqVVdVtVXQw8TtO4/hvwviRz2m0OTzKvfcsxSZa0a1ffBdxCcxT2TUn2TzIbOGvCz1sFfLh9/C5wIvBsVW0c7T9ZkqSp41UKJUkarbOBzyXZE3iA5ijnIJ9IchjNUdL/AO4G7gEWA3clCfAYcHq7/a3AZTRrWFcB11XVeJKLgBvb/Xy9qr7abv9NmiZ4VVWNJXkY+P5I/6WSJE2xVG19hpEkSeqTJCuBD1fVKV3PRZKk6eQpwZIkSZKkXvIIqyRJkiSplzzCKkmSJEnqJRtWSZIkSVIv2bBKkiRJknrJhlWSJEmS1Es2rJIkSZKkXrJhlSRJkiT10v8Bx+8dV6RZfdAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.cm as cm\n",
    "n_clusters = max(agglom.labels_)+1\n",
    "colors = cm.rainbow(np.linspace(0, 1, n_clusters))\n",
    "cluster_labels = list(range(0, n_clusters))\n",
    "\n",
    "# Create a figure of size 6 inches by 4 inches.\n",
    "plt.figure(figsize=(16,14))\n",
    "\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = pdf[pdf.cluster_ == label]\n",
    "    for i in subset.index:\n",
    "            plt.text(subset.horsepow[i], subset.mpg[i],str(subset['model'][i]), rotation=25) \n",
    "    plt.scatter(subset.horsepow, subset.mpg, s= subset.price*10, c=color, label='cluster'+str(label),alpha=0.5)\n",
    "#    plt.scatter(subset.horsepow, subset.mpg)\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we are seeing the distribution of each cluster using the scatter plot, but it is not very clear where is the centroid of each cluster. Moreover, there are 2 types of vehicles in our dataset, \"truck\" (value of 1 in the type column) and \"car\" (value of 1 in the type column). So, we use them to distinguish the classes, and summarize the cluster. First we count the number of cases in each group:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster_  type\n",
       "0         1.0      6\n",
       "1         0.0     47\n",
       "          1.0      5\n",
       "2         0.0     27\n",
       "          1.0     11\n",
       "3         0.0     10\n",
       "          1.0      7\n",
       "4         0.0      1\n",
       "5         0.0      3\n",
       "Name: cluster_, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdf.groupby(['cluster_','type'])['cluster_'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the characteristics of each cluster:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>horsepow</th>\n",
       "      <th>engine_s</th>\n",
       "      <th>mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster_</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>211.666667</td>\n",
       "      <td>4.483333</td>\n",
       "      <td>16.166667</td>\n",
       "      <td>29.024667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.531915</td>\n",
       "      <td>2.246809</td>\n",
       "      <td>27.021277</td>\n",
       "      <td>20.306128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>145.000000</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>17.009200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>203.111111</td>\n",
       "      <td>3.303704</td>\n",
       "      <td>24.214815</td>\n",
       "      <td>27.750593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>182.090909</td>\n",
       "      <td>3.345455</td>\n",
       "      <td>20.181818</td>\n",
       "      <td>26.265364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td rowspan=\"2\" valign=\"top\">3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>256.500000</td>\n",
       "      <td>4.410000</td>\n",
       "      <td>21.500000</td>\n",
       "      <td>42.870400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1.0</td>\n",
       "      <td>160.571429</td>\n",
       "      <td>3.071429</td>\n",
       "      <td>21.428571</td>\n",
       "      <td>21.527714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9.235000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>365.666667</td>\n",
       "      <td>6.233333</td>\n",
       "      <td>19.333333</td>\n",
       "      <td>66.010000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 horsepow  engine_s        mpg      price\n",
       "cluster_ type                                            \n",
       "0        1.0   211.666667  4.483333  16.166667  29.024667\n",
       "1        0.0   146.531915  2.246809  27.021277  20.306128\n",
       "         1.0   145.000000  2.580000  22.200000  17.009200\n",
       "2        0.0   203.111111  3.303704  24.214815  27.750593\n",
       "         1.0   182.090909  3.345455  20.181818  26.265364\n",
       "3        0.0   256.500000  4.410000  21.500000  42.870400\n",
       "         1.0   160.571429  3.071429  21.428571  21.527714\n",
       "4        0.0    55.000000  1.000000  45.000000   9.235000\n",
       "5        0.0   365.666667  6.233333  19.333333  66.010000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_cars = pdf.groupby(['cluster_','type'])['horsepow','engine_s','mpg','price'].mean()\n",
    "agg_cars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It is obvious that we have 3 main clusters with the majority of vehicles in those.\n",
    "\n",
    "__Cars__:\n",
    "- Cluster 1: with almost high mpg, and low in horsepower.\n",
    "- Cluster 2: with good mpg and horsepower, but higher price than average.\n",
    "- Cluster 3: with low mpg, high horsepower, highest price.\n",
    "    \n",
    "    \n",
    "    \n",
    "__Trucks__:\n",
    "- Cluster 1: with almost highest mpg among trucks, and lowest in horsepower and price.\n",
    "- Cluster 2: with almost low mpg and medium horsepower, but higher price than average.\n",
    "- Cluster 3: with good mpg and horsepower, low price.\n",
    "\n",
    "\n",
    "Please notice that we did not use __type__ , and __price__ of cars in the clustering process, but Hierarchical clustering could forge the clusters and discriminate them with quite high accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n",
      "'c' argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with 'x' & 'y'.  Please use a 2-D array with a single row if you really want to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mpg')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,10))\n",
    "for color, label in zip(colors, cluster_labels):\n",
    "    subset = agg_cars.loc[(label,),]\n",
    "    for i in subset.index:\n",
    "        plt.text(subset.loc[i][0]+5, subset.loc[i][2], 'type='+str(int(i)) + ', price='+str(int(subset.loc[i][3]))+'k')\n",
    "    plt.scatter(subset.horsepow, subset.mpg, s=subset.price*20, c=color, label='cluster'+str(label))\n",
    "plt.legend()\n",
    "plt.title('Clusters')\n",
    "plt.xlabel('horsepow')\n",
    "plt.ylabel('mpg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
    "\n",
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
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